US20190095691A1

US20190095691A1 - Derived virtual quality parameters for fingerprint matching

Info

Publication number: US20190095691A1
Application number: US16/185,969
Authority: US
Inventors: Hui Chen; Peter Zhen-Ping Lo
Original assignee: MorphoTrak LLC
Current assignee: MorphoTrak LLC
Priority date: 2015-11-16
Filing date: 2018-11-09
Publication date: 2019-03-28
Also published as: US20170330022A1; US9754151B2; US20170185821A1; US10127432B2; US9626549B1

Abstract

In some implementations, a computer-implemented method may include: identifying one or more neighboring minutiae within a particular octant neighborhood for the octant feature vector for each minutia included in a list of minutiae associated with a search fingerprint; computing, for each minutia included in the list of minutiae, a direction difference between each minutia included in the list of minutiae, and each of the one or more neighboring minutiae identified for the octant feature vector for each minutia included in the list of minutiae; computing, for each minutia included in the list of minutiae, a minutia quality confidence; and computing a fingerprint quality confidence.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No. 15/667,812, filed Aug. 3, 2017, which is a continuation of U.S. patent application Ser. No. 15/455,276, filed Mar. 10, 2017, now issued as U.S. Pat. No.: 9,754,151, which is continuation of U.S. patent application Ser. No. 14/942,449, filed Nov. 16, 2015, now issued as U.S. Pat. No. 9,626,549. The contents of each application are hearby incorporation by reference in their entirety.

FIELD

The present disclosure relates generally to fingerprint identification systems.

BACKGROUND

Pattern matching systems such as ten-print or fingerprint matching systems play a critical role in criminal and civil applications. For example, fingerprint identification is often used for identify and track suspects and in criminal investigations. Similarly, fingerprint verification is used in in civil applications to prevent fraud and support other security processes.

SUMMARY

Although significant improvements in fingerprint recognition have been achieved, the design of highly accurate matching systems that use only minutiae information remains challenging. For instance, embedded fingerprint matching systems utilize limited sets of features, such as minutiae and singularity points, from a fingerprint due to storage and computational constraints. However, common matching methods that use only minutiae information often use different minutia descriptors rather than integrating minutiae attributes to compute a similarity score. Consequently, these methods often omit analysis of mated and non-mated minutiae, which can impact matching accuracy.
When the minutia quality and fingerprint quality are not available, the accuracy of the fingerprint recognition without using the quality may be affected. Accordingly, one innovative aspect described throughout this disclosure includes to improve matching accuracy without using the quality derived from the fingerprint image. The improved minutiae matching techniques using derived virtual quality parameters. For instance, the derived virtual quality parameters may be used to compare a number of mated minutiae between a search fingerprint and a reference fingerprint, and a number of non-mated minutiae between the search fingerprint and the reference fingerprint. Since the number of mated and non-mated minutiae within the search fingerprint indicate different types of correspondence between the search fingerprint and the reference fingerprint, calculation of derived virtual quality parameters, and consideration of the virtual quality parameters within a similarity score calculation enables a stronger matching accuracy between the search fingerprint and the reference fingerprint.
Implementations may include one or more of the following features. For example, a computer-implemented method for determining fingerprint quality, the method implemented by an automatic fingerprint identification system including a processor, a memory coupled to the processor, an interface to a fingerprint scanning device, and a sensor associated with the fingerprint scanning device that indicates a fingerprint match, the method including: receiving a list of minutiae extracted from a search fingerprint; generating an octant feature vector for each minutia included in the list of minutiae; identifying one or more neighboring minutiae within a particular octant neighborhood for the octant feature vector for each minutia included in the list of minutiae; computing, for each minutia included in the list of minutiae, a direction difference between (i) each minutia included in the list of minutiae, and (ii) each of the one or more neighboring minutiae identified for the octant feature vector for each minutia included in the list of minutiae; assigning, for each minutia included in the list of minutiae, a minutia quality confidence based at least on one or more parameters; computing a fingerprint quality confidence based at least on (i) the value of an aggregate minutia quality confidence for each minutia included in the list of minutiae, and (ii) a number of minutia within the list of minutiae that are identified to have a sufficient number of neighboring minutiae, where the aggregate minutiae quality confidence for each minutia included in the list of minutiae represents a combination of the respective minutiae quality confidences for a single minutia and each of the one or more neighboring minutiae identified for the octant feature vector for the single minutia; and providing the fingerprint quality confidence to the fingerprint matching system.
Other versions include corresponding systems, and computer programs, configured to perform the actions of the methods encoded on computer storage devices.
One or more implementations may include the following optional features. For example, in some implementations, the one or more parameters include at least one of: a calculated distance difference for each minutia included in the list of minutiae; a number of its neighbor minutiae; a number of minutiae inside a close radius threshold; a number of minutiae outside a far radius threshold; or a direction difference.
In some implementations, the computer-implemented method may include computing a fingerprint similarity score between the fingerprint and a reference fingerprint based at least on the value of the fingerprint quality confidence.
In some implementations, the fingerprint similarity score between the fingerprint and the reference fingerprint is computed additionally based on (i) a number of minutiae within the list of minutiae that are identified as mated minutiae, and (ii) a number of minutiae within the list of minutiae that are identified as non-mated minutiae.
In some implementations, the fingerprint similarity score is adjusted based at least on (i) the value of the match quality confidence, and (ii) the value of the non-match minutia quality confidence.
In some implementations, adjusting the value of the fingerprint similarity score includes: estimating (i) an area of an overlapping region between the fingerprint and the reference fingerprint, (ii) an area of a fingerprint region, and (iii) an area of the reference fingerprint region; classifying, based at least on the overlapping region, the area of the fingerprint region, and the area of the reference fingerprint region, each of the minutiae included in the list of minutiae to one or more quality indicative groups; and adjusting the value of the fingerprint similarity score based at least on a number of minutiae classified as each of the one or more quality indicative groups.
In some implementations, the one or more quality indicative groups includes: a mated minutiae quality group that indicates a similarity between the fingerprint and the reference fingerprint; a first non-mated minutiae quality group that indicates that a particular minutia within the fingerprint has been identified to have a close reference minutiae, from the reference fingerprint, within the overlapping region; and a second non-mated minutiae quality group that indicates that a particular minutia within the fingerprint has been identified to not have a close reference minutia, from the reference fingerprint, within the overlapping region.
In some implementations, adjusting the value of the fingerprint similarity score includes increasing the value of the fingerprint similarity score based at least on the number of minutiae that classified within the mated minutiae quality group.
In some implementations, adjusting the value of the fingerprint similarity score includes decreasing the value of the fingerprint similarity score based at least on the number of minutiae that classified within the first non-mated minutiae quality group.
In some implementations, adjusting the value of the fingerprint similarity score includes decreasing the value of the fingerprint similarity score based at least on the number of minutiae that classified within the second non-mated minutiae quality group.
In some implementations, the value of the fingerprint similarity score is decreased by a first magnitude based at least on the number of minutiae that classified within the first non-mated minutiae quality group, and the value of the fingerprint similarity score is decreased by a second magnitude based at least on the number of minutiae that classified within the second non-mated minutiae quality group, where the second magnitude is greater than first magnitude.
The details of one or more implementations are set forth in the accompanying drawings and the description below. Other potential features and advantages will become apparent from the description, the drawings, and the claims.
Other implementations of these aspects include corresponding systems, apparatus and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram of an exemplary automatic fingerprint identification system.

FIG. 1B is a block diagram of an exemplary feature extraction process.

FIG. 2 is an exemplary illustration of geometric relationships between a reference minutia and a neighboring minutia.

FIG. 3 is a graphical illustration of the relationships represented in an exemplary octant feature vector (OFV).

FIG. 4 is an exemplary process of generating an octant feature vector (OFV).

FIG. 5 is an exemplary process of calculating similarity between two minutiae.

FIG. 6 is an exemplary alignment process for a pair of fingerprints.

FIG. 7 is an exemplary minutiae matching process for a pair of fingerprints.

FIG. 8A is an exemplary process for computing an image quality score for a search fingerprint.

FIG. 8B is an exemplary process for adjusting a similarity score between a search fingerprint and a reference fingerprint based on derived virtual quality parameters.

FIG. 9 is an exemplary process for generating derived virtual quality parameters for fingerprint matching.

In the drawings, like reference numbers represent corresponding parts throughout.

DETAILED DESCRIPTION

In general, one innovative aspect described throughout this disclosure includes improved minutiae matching techniques using derived virtual quality parameters. For instance, the derived virtual quality parameters may be generated based on comparing a number of mated minutiae between a search fingerprint and a reference fingerprint, and a number of non-mated minutiae between the search fingerprint and the reference fingerprint. Since the number of mated and non-mated minutiae within the search fingerprint indicate different types of correspondence between the search fingerprint and the reference fingerprint, calculation of derived virtual quality parameters, and consideration of the virtual quality parameters in computing a similarity score enables a stronger matching accuracy between the search fingerprint and the reference fingerprint.

System Architecture

FIG. 1 is a block diagram of an exemplary automatic fingerprint identification system 100. Briefly, the automatic fingerprint identification system 100 may include a computing device including a memory device 110, a processor 115, a presentation interface 120, a user input interface 130, and a communication interface 135. The automatic fingerprint identification system 100 may be configured to facilitate and implement the methods described through this specification. In addition, the automatic fingerprint identification system 100 may incorporate any suitable computer architecture that enables operations of the system described throughout this specification.
The processor 115 may be operatively coupled to memory device 110 for executing instructions. In some implementations, executable instructions are stored in the memory device 110. For instance, the automatic fingerprint identification system 100 may be configurable to perform one or more operations described by programming the processor 115. For example, the processor 115 may be programmed by encoding an operation as one or more executable instructions and providing the executable instructions in the memory device 110. The processor 115 may include one or more processing units, e.g., without limitation, in a multi-core configuration.
The memory device 110 may be one or more devices that enable storage and retrieval of information such as executable instructions and/or other data. The memory device 110 may include one or more tangible, non-transitory computer-readable media, such as, without limitation, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), a solid state disk, a hard disk, read-only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), and/or non-volatile RAM (NVRAM) memory. The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program.
The memory device 110 may be configured to store a variety of data including, for example, matching algorithms, scoring algorithms, scoring thresholds, perturbation algorithms, fusion algorithms, virtual minutiae generation algorithms, minutiae overlap analysis algorithms, and/or virtual minutiae analysis algorithms. In addition, the memory device 110 may be configured to store any suitable data to facilitate the methods described throughout this specification.
The presentation interface 120 may be coupled to processor 115. For instance, the presentation interface 120 may present information, such as a user interface showing data related to fingerprint matching, to a user 102. For example, the presentation interface 120 may include a display adapter (not shown) that may be coupled to a display device (not shown), such as a cathode ray tube (CRT), a liquid crystal display (LCD), an organic LED (OLED) display, and/or a hand-held device with a display. In some implementations, the presentation interface 120 includes one or more display devices. In addition, or alternatively, the presentation interface 120 may include an audio output device (not shown), e.g., an audio adapter and/or a speaker.
The user input interface 130 may be coupled to the processor 115 and receives input from the user 102. The user input interface 130 may include, for example, a keyboard, a pointing device, a mouse, a stylus, and/or a touch sensitive panel, e.g., a touch pad or a touch screen. A single component, such as a touch screen, may function as both a display device of the presentation interface 120 and the user input interface 130.
In some implementations, the user input interface 130 may represent a fingerprint scanning device that is used to capture and record fingerprints associated with a subject (e.g., a human individual) from a physical scan of a finger, or alternately, from a scan of a latent print. In addition, the user input interface 130 may be used to create a plurality of reference records.
A communication interface 135 may be coupled to the processor 115 and configured to be coupled in communication with one or more other devices such as, for example, another computing system (not shown), scanners, cameras, and other devices that may be used to provide biometric information such as fingerprints to the automatic fingerprint identification system 100. Such biometric systems and devices may be used to scan previously captured fingerprints or other image data or to capture live fingerprints from subjects. The communication interface 135 may include, for example, a wired network adapter, a wireless network adapter, a mobile telecommunications adapter, a serial communication adapter, and/or a parallel communication adapter. The communication interface 135 may receive data from and/or transmit data to one or more remote devices. The communication interface 135 may be also be web-enabled for remote communications, for example, with a remote desktop computer (not shown).
The presentation interface 120 and/or the communication interface 135 may both be capable of providing information suitable for use with the methods described throughout this specification, e.g., to the user 102 or to another device. In this regard, the presentation interface 120 and the communication interface 135 may be used to as output devices. In other instances, the user input interface 130 and the communication interface 135 may be capable of receiving information suitable for use with the methods described throughout this specification, and may be used as input devices.
The processor 115 and/or the memory device 110 may also be operatively coupled to the database 150. The database 150 may be any computer-operated hardware suitable for storing and/or retrieving data, such as, for example, pre-processed fingerprints, processed fingerprints, normalized fingerprints, extracted features, extracted and processed feature vectors such as octant feature vectors (OFVs), threshold values, virtual minutiae lists, minutiae lists, matching algorithms, scoring algorithms, scoring thresholds, perturbation algorithms, fusion algorithms, virtual minutiae generation algorithms, minutiae overlap analysis algorithms, and virtual minutiae analysis algorithms.
The database 150 may be integrated into the automatic fingerprint identification system 100. For example, the automatic fingerprint identification system 100 may include one or more hard disk drives that represent the database 150. In addition, for example, the database 150 may include multiple storage units such as hard disks and/or solid state disks in a redundant array of inexpensive disks (RAID) configuration. In some instances, the database 150 may include a storage area network (SAN), a network attached storage (NAS) system, and/or cloud-based storage. Alternatively, the database 150 may be external to the automatic fingerprint identification system 100 and may be accessed by a storage interface (not shown). For instance, the database 150 may be used to store various versions of reference records including associated minutiae, octant feature vectors (OFVs) and associated data related to reference records.

Feature Extraction

In general, feature extraction describes the process by which the automatic fingerprint identification system 100 extracts a list of minutiae from each of reference fingerprint, and the search fingerprint. As described, a “minutiae” represent major features of a fingerprint, which are used in comparisons of the reference fingerprint to the search fingerprint to determine a fingerprint match. For example, common types of minutiae may include, for example, a ridge ending, a ridge bifurcation, a short ridge, an island, a ridge enclosure, a spur, a crossover or bridge, a delta, or a core.
FIG. 1B is a block diagram of an exemplary feature extraction process 150. As shown, after receiving an input fingerprint 104, the automatic fingerprint identification system 100 initially identifies a set of features 112 within the fingerprint, generates a list of minutiae 114, and extracts a set of feature vectors 116. For instance, the automatic fingerprint identification system 100 may generate a list of minutia 114 for each of the reference fingerprint (or “reference record”) and a search fingerprint (or “search record”).
In some implementations, the feature vectors 116 may be described using feature vector that is represented by Mf_i=(x_i, y_i, θ_i). As described, the feature vector Mf_iincludes a minutia location that is defined by coordinate geometry such as (x_i, y_i), and a minutiae direction that is defined by the angle θ_i∈[0,2π]. In other examples, further minutiae characteristics such as, quality, ridge frequency, and ridge curvature may also be used to describe feature vector Mf_i. The extracted feature vectors may be used to generate octant feature vectors (OFVs) for each of identified minutia within the search and reference fingerprints.

Octant Feature Vector (OFV) Overview

The automatic fingerprint identification system 100 may compare the search and reference records based on initially generating feature vectors associated with minutiae that are extracted from the search and reference records, respectively. For instance, as described throughout this specification, in some implementations, octant feature vectors (OFVs) may be used to as feature vectors that define attributes of the extracted minutiae. However, in other implementations, other minutiae descriptors may be used.
OFVs encode geometric relationships between reference minutiae and the nearest neighboring minutiae to the reference minutiae in a particular sector (referred to as the “octant neighborhood”) of the octant. Each sector of the octant used in an OFV spans 45 degrees of a fingerprint region. The nearest neighboring minutiae may be assigned to one sector of the octant based on their orientation difference. The geometric relationship between a reference minutia and its nearest minutia in each octant sector may be described by relative features including, for example, distance between the minutiae and the orientation difference between minutiae. The representation achieved by the use of an OFV is invariant to transformation. In addition, this representation is insensitive to a nonlinear distortion because the relative features are independent from any transformation.
Pairs of reference minutiae and nearest neighboring minutiae may be identified as “mated minutiae pairs.” The mated minutiae pairs in a reference record and a search record may be identified by comparing the respective OFVs of minutiae extracted from the reference record and the search record. The transformation parameters may be estimated by comparing attributes of the corresponding mated minutiae. For example, the transformation parameters may indicate the degree to which the search record has been transformed (e.g., perturbed or twisted) as relative to a particular reference record. In other examples, the transformation parameters may be applied to verify that, for a particular pair of a reference record and a search record (a “potential matched fingerprint pair”), mated minutiae pairs exhibit corresponding degrees of transformation. Based on the amount of corresponding mated minutiae pairs in each potential matched fingerprint pair, and the consistency of the transformation, a similarity score may be assigned. In some implementations, the pair of potential matched fingerprint pairs with the highest similarity score may be determined as a candidate matched fingerprint pair.
The automatic fingerprint identification system 100 may calculate an OFV for each minutia that encodes the geometric relationships between the reference minutia and its nearest minutiae in each sector of the octant. For instance, the automatic fingerprint identification system 100 may define eight octant sectors and assigns the nearest minutiae to one sector of the octant based on the location of each minutiae within the sectors. The geometric relationship between a reference minutia and its nearest minutia in each octant sector is represented by the relative features. For example, in some implementations, the OFV encodes the distance, the orientation difference, and the ridge count difference between the reference feature and the nearest neighbor features. Because the minutia orientation can flexibly change up to 45° due to the octant sector approach, relative features are independent from any transformation.
The automatic fingerprint identification system 100 may use the OFVs to determine the number of possible corresponding minutiae pairs. Specifically, the automatic fingerprint identification system 100 may evaluate the similarity between two respective OFVs associated with the search record and the file record. The automatic fingerprint identification system 100 may identify all possible local matching areas of the compared fingerprints by comparing the OFVs. The automatic fingerprint identification system 100 may also an individual similarity score for each of the mated OFV pairs.
The automatic fingerprint identification system 100 may cluster all OFVs of the matched areas with similar transformation effects (e.g., rotation and transposition) into an associated similar bin. Note that the precision of the clusters of the bins (e.g., the variance of the similar rotations within each bin) is a proxy for the precision of this phase. Automatic fingerprint identification system 100 therefore uses bins with higher numbers of matched OFVs (e.g., clusters with the highest counts of OFVs) for the first phase global alignment.
The automatic fingerprint identification system 100 may use the location and angle of each selected bin as the parameters of a reference point (an “anchor point”) to perform a global alignment procedure. More specifically, the automatic fingerprint identification system 100 may identify the global alignment based on the bins that include the greatest number of the global mated minutiae pairs, and the location and angle associated with each of those bins. Based on the number of global paired minutiae found and the total of individual similarity scores calculated for the corresponding OFVs within the bin or bins, the automatic fingerprint identification system 100 may identify the transformations (e.g., the rotations of the features) with the best alignment.
In a second phase, the automatic fingerprint identification system 100 performs a more precise pairing using the transformations with the best alignment to obtain a final set of the globally aligned minutiae pairs. In this phase, automatic fingerprint identification system 100 performs a pruning procedure to find geometrically consistent minutiae pairs with tolerance of distortion for each aligned minutiae set that factors in the local and global geometrical index consistency. By performing such alignment globally and locally, automatic fingerprint identification system 100 determines the best set of global aligned minutiae pairs. Automatic fingerprint identification system 100 uses the associated mini-scores of the global aligned pairs to calculate the global similarity score. Furthermore, automatic fingerprint identification system 100 factors in a set of absolute features of the minutiae, including the quality, ridge frequency, and the curvatures in the computation of the final similarity score.

OFV Generation

The automatic fingerprint identification system 100 may generate an octant feature vector (OFV) for each minutia of the features extracted. Specifically, as described above, the automatic fingerprint identification system 100 may generate OFVs encoding the distance and the orientation difference between the reference minutiae and the nearest neighbor in each of eight octant sectors. Alternately, the automatic fingerprint identification system 100 may generate feature vectors with different numbers of sectors.
FIG. 2 is an exemplary illustration 200 of geometric relationships between a reference minutia 210 and a neighboring minutia 220. The geometric relationships may be used to construct a rotation and translation invariant feature vector that includes relative attributes (d_ij, α_ij, β_ji) between the reference minutia 210 and the neighboring minutia 220.
As depicted in FIG. 2, the automatic fingerprint identification system 100 may compute a Euclidean distance 230 between the reference minutia 210 and the neighboring minutia 220, a minimum rotation angle 240 for the neighboring minutia, and a minimum rotation angle 250 for the reference minutia 210. In addition, the automatic fingerprint identification system may compute a ridge count 260 across the reference minutia 210 and the neighboring minutia 220.
Specifically, the rotation and translation invariant feature vector may be represented as vector 1 (represented below). In some implementations, an OFV is created to describe the geometric relationship between. Further, M_irepresents a reference minutia and M_jrepresents a nearest neighbor minutiae in one of the octant sectors. The OFV for each sector may be described in the given vector from vector 1:
Vector 1: (d_ij, α_ij, β_ji)

- where d_ijdenotes the Euclidean distance 230,
- where α_ij=λ(θ_i, θ_j) denotes the minimum rotation angle 240 required to rotate a line of direction θ_iin a particular direction (e.g., counterclockwise in FIG. 2) to make the line parallel with a line of direction θ_j, and
- where β_ij=(θ_i, ∠(M_i, M_j)) denotes the minimum rotation angle 250, where ∠(M_i, M_j) denotes the direction from the reference minutia 210 to the neighboring minutia 220, and where λ(a, b) denotes the same meaning as defined in α_ij

Specifically, because each element calculated in the feature vector is a relative measurement between the reference minutia 210 and the neighboring minutia 220, the feature vector is independent from the rotation and translation of the fingerprint. Elements 230, 240, and 250 may be referred to as relative features and are used to compute the similarity between pair of reference minutia 210 and the neighboring minutia 220. In some implementations, other minutiae features such as absolute features may additionally or alternatively be used by the automatic fingerprint identification system 100 to weight the computed similarity score between a pair of mated minutiae that includes reference minutia 210 and the neighboring minutia 220.
FIG. 3 is a graphical illustration of the relationships represented in an exemplary octant feature vector (OFV) 300. The OFV 300 may be generated for a reference minutia 310, which corresponds to the reference minutia 210 as shown in FIG. 2. As shown, the OFV 300 represents relationships between the reference minutiae 310 and its nearest neighboring minutiae 322, 332, 342, 352, 362, 372, 382, and 392 in sectors 320, 330, 340, 350, 360, 370, 380, and 390, respectively. However, the graphical illustration of OFV 300 does not depict the details of the geographic relationships, which are described within respect to FIG. 2. Although FIG. 3 indicates a neighboring minutia within each octant sector, in some instances, there may be no neighboring minutiae within a particular sector. In such instances, the OFV for the particular sector without a neighboring minutia is set to zero. Otherwise, because the neighboring minutiae 322, 332, 342, 352, 362, 372, 382, and 392 may not overlap with reference minutiae 310, the OFV is greater than zero.
FIG. 4 is an exemplary process 400 of generating an octant feature vector (OFV). Briefly, the process 400 may include identifying a plurality of minutiae from the input fingerprint image (410), selecting a particular minutia from the plurality of minutiae (420), defining a set of octant sectors for the plurality of minutiae (430), assigning each of the plurality of minutiae to an octant sector (440), identifying a neighboring minutiae to the particular minutia for each octant sector (450), and generating an octant feature vector for the particular minutia (460).
In more detail, the process 400 may include the process may include identifying a plurality of minutiae from the input fingerprint image (410). For instance, the automatic fingerprint identification system 100 may receive the input fingerprint 402 and generate a list of minutiae 412 using the techniques described previously with respect to FIG. 1B.
The process 400 may include selecting a particular minutia from the plurality of minutiae (420). For instance, the automatic fingerprint identification system 100 may select a particular minutiae within the list of minutiae 412.
The process 400 may include defining a set of octant sectors for the plurality of minutiae (430). For instance, the automatic fingerprint identification system 100 may generate a set of octant sectors 432 that include individual octant sectors k₀to k₇as shown in FIG. 4. The set of octant sectors 432 may be generated in reference to the particular minutia that is selected in step 420.
The process 400 may include assigning each of the plurality of minutiae to an octant sector (440). For instance, the automatic fingerprint identification system 100 may assign each of the plurality of minutiae from the list of minutiae 412 into corresponding octant sectors within the set of octant sectors 432. The assigned minutiae may be associated with the corresponding octant sectors in a list 442 that includes the number of minutiae that are identified within each individual octant sector. For example, as shown in FIG. 4, the exemplary octant sector k₁has no identified minutiae, whereas the exemplary k₆includes two identified minutiae within the octant sector. The graphical illustration 444 represents the locations of the plurality of minutiae, relative to the particular selected minutia, M_i, within the individual octant sectors.
The process 400 may include identifying a neighboring minutiae to the particular minutia for each octant sector (450). For instance, the automatic fingerprint identification system 100 may identify, from all the neighboring minutiae within each octant sector, the neighboring minutia that is the closest neighboring minutia based on the distance between each neighboring minutia and the particular selected minutia, M_i. For example, for the octant sector k₆, the automatic fingerprint identification system 100 may determine that the minutia, M₆is the closest neighboring minutia based on the distance between M_iand M₆. The closest neighboring minutiae for all of the octant sectors may be aggregated within a list of closest neighboring minutiae 452 that identifies each of the closest neighboring minutiae.
The process 400 may include generating an octant feature vector for the particular minutia (460). For instance, the automatic fingerprint identification system 100 may generate an octant feature vector 462, based on the list of closest neighboring minutiae 452, which includes a set of relative features such as the Euclidean distance 230, the minimum rotation angle 240, and the minimum rotation angle 250 as described previously with respect to FIG. 2.
As described above with respect to FIGS. 2-4, OFVs for minutiae may be used to characterize local relationships with neighboring minutiae, which are invariant to the rotation and translation of the fingerprint that includes the minutiae. The OFVs are also insensitive to distortion, since the nearest neighboring minutiae are assigned to multiple octant sectors in various directions, thereby allowing flexibility of orientation of up to 45°. In this regard, the OFVs of minutiae within a fingerprint may be compared against the OFVs of minutiae within another fingerprint (e.g., a search fingerprint) to determine a potential match between the two fingerprints. Descriptions of the general fingerprint matching process, and the OFV matching process are provided below.

Fingerprint Identification and Matching

In general, the automatic fingerprint identification system 100 may perform fingerprint identification and matching in two stages: (1) an enrollment stage, and (2) an identification/verification stage.
In the enrollment stage, an individual (or a “registrant”) has their fingerprints and personal information enrolled. The registrant may be an individual manually providing their fingerprints for scanning or, alternately, an individual whose fingerprints were obtained by other means. In some examples, registrants may enroll fingerprints using latent prints, libraries of fingerprints, and any other suitable repositories and sources of fingerprints. As described, the process of “enrolling” and other related terms refer to providing biometric information (e.g., fingerprints) to an identification system (e.g., the automatic fingerprint identification system 100).
The automatic fingerprint identification 100 system may extract features such as minutiae from fingerprints. As described, “features” and related terms refer to characteristics of biometric information (e.g., fingerprints) that may be used in matching, verification, and identification processes. The automatic fingerprint identification system 100 may create a reference record using the personal information and the extracted features, and save the reference record into the database 150 for subsequent fingerprint matching, verification, and identification processes.
In some implementations, the automatic fingerprint identification system 100 may contain millions of reference records. As a result, by enrolling a plurality of registrants (and their associated fingerprints and personal information), the automatic fingerprint identification system 100 may create and store a library of reference records that may be used for comparison to search records. The library may be stored at the database 150 associated.
In the identification stage, the automatic fingerprint identification system 100 may use the extracted features and personal information to generate a record known as a “search record”. The search record represents a source fingerprint for which identification is sought. For example, in criminal investigations, a search record may be retrieved from a latent print at a crime scene. The automatic fingerprint identification may compare the search record with the enrolled reference records in the database 150. For example, during a search procedure, a search record may be compared against the reference records stored in the database 150. In such an example, the features of the search record may be compared to the features of each of the plurality of reference records. For instance, minutiae extracted from the search record may be compared to minutiae extracted from each of the plurality of reference records.
As described, a “similarity score” is a measurement of the similarity of the fingerprint features (e.g., minutiae) between the search record and each reference record, represented as a numerical value to degree of similarity. For instance, in some implementations, the values of the similarity score may range from 0.0 to 1.0, where a higher magnitude represents a greater degree of similarity between the search record and the reference record.
The automatic fingerprint identification system 100 may compute individual similarity scores for each comparison of features (e.g., minutiae), and aggregate similarity scores (or “final similarity scores”) between the search record to each of the plurality of reference records. In this regard, the automatic fingerprint identification system 100 may generate similarity scores of varying levels of specificity throughout the matching process of the search record and the plurality of reference records.
The automatic fingerprint identification system 100 may also sort each of the individual similarity scores based on the value of the respective similarity scores of individual features. For instance, the automatic identification system 100 may compute individual similarity scores between respective minutiae between the search fingerprint and the reference fingerprint, and sort the individual similarity scores by their respective values.
A higher final similarity score indicates a greater overall similarity between the search record and a reference record while a lower final similarity score indicates a lesser over similarity between the search record and a reference record. Therefore, the match (e.g., the relationship between the search record and a reference record) with the highest final similarity score is the match with the greatest relationship (based on minutiae comparison) between the search record and the reference record.

Minutiae and OFV Matching

In general, the OFVs of minutiae may be compared between two fingerprints to determine a potential match between a reference fingerprint and a search fingerprint. The automatic fingerprint identification system 100 may compare the OFVs of corresponding minutiae from the reference fingerprint and the search fingerprint to compute an individual similarity score that reflects a confidence that the particular reference minutiae corresponds to the particular search minutiae that is being compared to. The automatic fingerprint identification system 100 may then compute aggregate similarity scores, between a list of reference minutiae and a list of search minutiae, based the values of the individual similarity scores for each minutiae. For instance, as described more particularly below, various types of aggregation techniques may be used to determine the aggregate similarity scores between the reference fingerprint and the search fingerprint.
FIGS. 5-7 generally describe different processes that may be used to during fingerprint identification and matching procedures. For instance, FIG. 5 illustrates an exemplary process of calculating an individual similarity score between a reference minutia and a search minutia. FIG. 6 illustrates an exemplary alignment process between two fingerprints using extracted minutiae from the two fingerprints, and FIG. 7 illustrates an exemplary minutiae matching technique that may be employed after the alignment procedure represented in FIG. 7.
Referring to FIG. 5, a similarity determination process 500 may be used to compute an individual similarity score between a reference minutia 502 a from a reference fingerprint, and a search minutia 502 b from a search fingerprint. A reference OFV 504 a and a search OFV 504 b may be generated for the reference minutia 502 a and the search minutia 504 b, respectively, using the techniques described with respect to FIG. 4. As shown, the search and reference OFVs 504 a and 504 b include individual octant sectors k₀to k₇as described as illustrated in FIG. 3. Each of the reference OFV 504 a and the search OFV 504 b may include parameters such as, for example, the Euclidian distance 230, the minimum rotation angle 240, and the minimum rotation angle 250 as described with respect to FIG. 2. As shown in FIG. 5, exemplary parameters 506 a and 506 b may represent the parameters for octant sector k₀of the reference OFV 502 a and the search OFV 502 b, respectively.
The similarity score determination may be performed by an OFV comparison module 520, which may be a software module or component of the automatic fingerprint identification system 100. In general, the similarity score calculation includes four steps. Initially, the Euclidian distance values of a particular octant sector may be evaluated (522). Corresponding sectors between the reference OFV 504 a and the similarity OFV 504 b may then be compared (524). A similarity score between a particular octant sector within the reference OFV 504 a and its corresponding octant sector in the search OFV 504 b may then be computed (526). Finally, the similarity scores for the between other octant sectors of the reference OFV 504 a and the search OFV 504 b may then be computed and combined to generate the final similarity score between the reference OFV 504 a and the search OFV 504 b (528).
With respect to step 522, the similarity module 520 may initially determine if the Euclidian distance values within the parameters 506 a and 506 b are non-zero values. For instance, as shown in decision point 522 a, the Euclidean distance associated with the octant sector of the reference OFV 504 a, d_RO, is initially be evaluated. If this value is equal to zero, then the similarity score for the octant sector k₀is set to zero. Alternatively, if the value of d_ROis greater than zero, then the similarity module 520 proceeds to decision point 522 b, where the Euclidean distance associated with the octant sector of the reference OFV 504 a, d_SO, is evaluated. If the value of d_SOis not greater than zero, then the OFV similarity module 520 evaluates the value of the Euclidean distance d_S1, which is included in an adjacent sector k₁to the octant sector k₀within the reference OFV 504 b. Although FIG. 5 represents only one of the adjacent sectors being selected, because each octant sector includes two adjacent octant sectors as shown in FIG. 3, in other implementations, octant sector k₇may also be evaluated. If the value of the Euclidean distance within the adjacent octant sector is not greater than zero, then the similarity module 520 sets the value of individual similarity score S_RS01, between the octant sector k₀of the reference OFV 504 a and the octant sector k₁of the reference OFV 504 b, to zero.
Alternatively, if the either the value of Euclidean distance d_ROwithin the octant sector k₀of the reference OFV 504 a, or the Euclidean distance d_S1within the adjacent octant sector k₁of the search OFV 504 b is determined to be a non-zero value within the decision points 522 b and 522 c, respectively, then the similarity module proceeds to step 524.
In some instances, a particular octant vector may include zero corresponding minutiae within the search OFV 504 b due to localized distortions within the search fingerprint. In such instances, where the corresponding minutiae may have drifted to an adjacent octant sector, the similarity module 520 may alternatively compare the features of the octant vector of the reference OFV 504 a to a corresponding adjacent octant vector of the search OFV 504 b as shown in step 524 b.
If proceeding through decision point 522 b, the similarity module 520 may proceed to step 524 a where the corresponding octant sectors between the reference OFV 504 a and the search OFV 504 b are compared. If proceeding through decision point 522 c, the similarity module 620 may proceed to step 524 b where the octant sector k₀of the reference OFV 504 a is compared to the corresponding adjacent octant sector k₁the search OFV 504 b. During either process, the similarity module 520 may compute the difference between the parameters that are included within each octant sector of the respective OFVs. For instance, as shown, the difference between the Euclidean distances 230, Δd, the difference between the minimum rotation angles 240, Δα, and the difference between the minimum rotation angles 250, Δβ, may be computed. Since these parameters represent geometric relationships between pairs of minutiae, the differences between them represent distance and orientation differences between the reference and search minutiae with respect to particular octant sectors.
In some implementations, dynamic threshold values for the computed feature differences may be used to handle nonlinear distortions within the search fingerprint in order to find mated minutiae between the search and reference fingerprint. For instance, the values of the dynamic thresholds may be adjusted to larger or smaller values to adjust the sensitivity of the mated minutiae determination process. For example, if the value of the threshold for the Euclidean distance is set to a higher value, than more minutiae within a particular octant sector may be determined to be a neighboring minutia to a reference minutiae based on the distance being lower than the threshold value. Likewise, if the threshold is set to a smaller value, then a smaller number of minutiae within the particular octant sector may be determined to be neighboring minutia based on the distance to the reference minutia being greater than the threshold value.
After either comparing the corresponding octant sectors in step 524 a or comparing the corresponding adjacent octant sectors in step 524 b, the similarity module 520 may then compute an individual similarity score between the respective octant sectors in steps 526 a and 526 b, respectively. For instance, the similarity score may be computed based on the values of the feature differences as computed in steps 524 a and 524 b. For instance, the similarity score may represent the feature differences and indicate minutiae that are likely to be distorted minutiae. For example, if the feature differences between a reference minutiae and corresponding search minutia within a particular octant sector are close the dynamic threshold values, the similarity module 520 may identify the corresponding search minutia as a distortion candidate.
After computing the similarity score for either the corresponding octant sectors, or the corresponding adjacent sectors in steps 526 a and 526 b, respectively, the similarity module 520 may repeat the steps 522-526 for all of the other octant sectors included within the reference OFV 504 a and the search OFV 504 b. For instance, the similarity module 520 may iteratively execute the steps 522-526 until the similarity scores between each corresponding octant sector and each corresponding adjacent octant sector are computed for the reference OFV 504 a and the search OFV 504 b.
The similarity module may then combine the respective similarity scores for each corresponding octant sectors and/or the corresponding adjacent octant sectors to generate a final similarity score between the reference minutia and the corresponding search minutia. This final similarity score is also referred to as the “individual similarity score” between corresponding minutiae within the search and reference fingerprints as described in other sections of this specification. The individual similarity score indicates a strength of the local matching of the corresponding OFV.
In some implementations, the particular aggregation technique used by the similarity module 522 to generate the final similarity score (or the “individual similarity score”) may vary. For example, in some instances, the final similarity score may be computed based on adding the values of the similarity scores for the corresponding octant sectors and the corresponding adjacent octant sectors, and normalizing the sum by a sum of a total number of possible mated minutiae for the reference minutia and a total of number of possible mated minutiae for the search minutia. In this regard, the final similarity score is weighted by considering the number of mated minutiae and the total number of possible mated minutiae.
FIG. 6 illustrates an exemplary alignment process 600 between a reference fingerprint and a search fingerprint. Briefly, the process 600 may initially compare a list of reference OFVs 604 a associated with a list of reference minutiae 602 a and a list of search OFVs 604 b associated with a list of search minutiae 604 b, and generate a list of all possible mated minutiae 612. A global alignment module 620 may then perform a global alignment procedure on the list of all possible mated minutia 612 to generate a clustered list of all possible mated minutiae 622, and determine two best alignment rotations 622 for the search fingerprint relative to the reference fingerprint. A precision alignment module may then use the two best rotations 624 perform a second alignment procedure to generate a list that includes the best-aligned pair 632 for the plurality of bins, which are provided as outputs of the alignment process.
As described previously with respect to FIG. 5, the OFVs of corresponding minutiae within the reference fingerprint and the search fingerprint may be compared by the OFV comparison module 610 to generate the list of all possible mated minutiae 612. As described, “mated minutiae” refer to a pair of minutiae that includes a particular reference minutia and a corresponding search minutia based at least on the OFV comparison performed by the OFV comparison module 610, and the value of the individual similarity score between the two respective OFVs of the reference and search minutiae. The individual similarity score indicates a strength of the local matching of the corresponding OFV. In addition, the list of all possible mated minutiae 612 includes all of the minutiae within the octant sectors that are identified as neighboring minutiae to a particular reference minutia and have a non-zero similarity score, although additional mated minutiae may exist with similarity score values equal to zero. Although as shown in the FIG., the list of all possible minutiae 612 includes one search minutia per reference minutia, in some instances, multiple mated minutiae may exist within the list of all possible minutiae 612 for a single reference minutia.
The global alignment module 620 performs a global alignment process on the list of all possible mated minutiae 612, which estimates a probable (or best rotation) alignment between the reference fingerprint and the search fingerprint based on comparing the angle offsets between the individual minutiae within the mated minutiae. For instance, the global alignment module 620 may initially compute an angle offset for each mated minutiae pair based on the individual similarity scores between a particular reference minutia and its corresponding search minutia.
Each of the mated minutiae within the list of all possible mated minutiae 612 may then be grouped into a histogram bin that is associated with a particular angle offset range. For instance, two mated minutiae pairs within the list of all possible mated minutiae 612 may be grouped into the same histogram bin if their respective individual similarity scores indicate a similar angular offset between the individual minutia within each mated minutiae pair. In some instances, the number of histogram bins for the list of all possible mated minutiae 612 is used to estimate a fingerprint quality score for the search fingerprint. For example, if the quality of the search fingerprint is excellent, then the angular offset among each of the mated minutiae within the list of all possible mated minutiae 612 should be consistent, and majority of the mated minutiae will be grouped into a single histogram bin. Alternatively, if the fingerprint quality is poor, then the number of histogram bins would increase, representing significant variations between the angular offset values between the mated minutiae within the list of all possible mated minutiae 612.
In addition to grouping the mated minutiae into a particular histogram bin, the global alignment module 620 may determine a set of rigid transformation parameters, which indicate geometric differences between the reference minutiae and the search minutiae with similar angular offsets. The rigid transformation parameters thus indicate a necessary rotation of the search fingerprint at particular locations, represented by the locations of the minutiae, in order to geometrically align the search fingerprint to the reference fingerprint. Since the rigid transformation parameters are computed for all possible mated minutiae, the necessary rotation represents a global alignment between the reference fingerprint and the search fingerprint. The global alignment module 620 may then generate a clustered list of all possible mated minutiae, which groups the mated minutiae by the histogram bin based on the respective angle offsets, and includes a set of rigid transformation parameters. In some implementations, the histogram represented by the plurality of bins may be smoothened by a Gaussian function.
The global alignment module 620 may use the clustered list of all possible mated minutiae to determine two best rotations 624. For instance, the two best rotations 624 may be determined by using the rigid transformation parameters to calculate a set of alignment rotations for the search fingerprint using each histogram bin as a reference point. Each alignment rotation may then be applied to the search fingerprint to generate a plurality of transformed search fingerprints that is individually mapped to each alignment rotation. For example, in some instances, the number of alignment rotations corresponds to the number of histogram bins generated for the list of all possible mated minutiae 612. In such instances, the number of transformed search fingerprints generated corresponds to the number of histogram bins included in the cluster list of all possible mated minutiae 622. Each set of transformed search fingerprints may then be compared to the reference fingerprint to determine the two best rotations 624. For example, as described more particularly with respect to FIG. 7, each transformed search fingerprint may be compared to the reference fingerprint using a minutiae matching technique to determine which particular alignment rotations generate the greatest number of correctly matched minutiae between a particular transformed search fingerprint and the reference fingerprint. The global alignment module 620 may then extract the two best alignment rotations 624, which are then used by the precision alignment module 630.
In some implementations, different matching constraints may be used with the minutiae matching techniques to determine the two best alignment rotations 624.
The precision alignment module 630 may then use the two best alignment rotations 624 to perform a precision alignment process that iteratively rotates individual minutiae within the search fingerprint around the two best alignment rotations 632 several times with small angle variations to obtain a more precise pairing between individual search minutiae and their corresponding reference minutiae. For example, in some, twelve rotations may be used with two degree angle variations. The minutiae that are associated with the precise pairing between the search fingerprint and the reference fingerprint are determined to be the list of best-aligned minutiae 632, which are provided for output by the process 600. The list of best-aligned minutiae 632 represent transformations of individual search minutiae within the search fingerprint that most closely pair with the corresponding reference minutiae of the reference fingerprint as a result of the global alignment and the precision alignment processes.
FIG. 7 illustrates an exemplary minutiae matching process 700. The minutiae matching process 700 may be performed after the fingerprint alignment process 600 as described in FIG. 6 to remove false correspondences included within a list of aligned minutiae that is outputted from alignment process. For instance, fingerprints from two fingers of an individual may share local structures, which can result in false correspondence minutiae between a search fingerprint of one finger and a reference fingerprint of another finger. To resolve this, the minutiae matching process 700 includes a two-stage pruning process to remove false correspondence minutiae pairs within a list of all possible mated minutiae.
Briefly, the process 700 may include a local geometric module 710 receiving a list of aligned minutiae 710, and generating a modified list of all possible minutiae 712 that does not include false correspondence minutiae. A global consistency pairing module may then sort the modified list of all possible minutiae 712 by values of the respective individual similarity scores to generate a sorted modified list of all possible minutiae 722. The global consistency pairing module 720 may remove minutiae pairs with duplicate indexes 724, and group the list of mated minutiae based on conducting a global geometric consistency evaluation to generate a list of geometrically consistent groups 726, which are then outputted with a top average similarity score from one of the geometrically consistent groups.
Initially, the local geometric module 710 may select the best-paired minutiae from the list of all possible mated minutiae. For instance, after aligned the search fingerprint and the reference fingerprint as described in FIG. 6, the local geometric module 710 may scan the list of all possible minutiae and identify the two minutiae pairs with the minimum orientation difference.
In some instances, the identification of the best-paired minutiae may additionally be subject to satisfying a set of constraints. For example, one constraint may be that the index of the first pair is different from that of the first pair. In other examples, the rigid transformation parameters of the two best-paired minutiae pairs may be compared to threshold values to ensure that the two identified pairs are geometrically consistent.
After identifying the two best-paired minutiae pairs, the local geometric module 710 may use the two best-paired minutiae pairs as reference pairs to remove other minutiae pairs from the list of all possible mated minutiae. In some instances, particular pairs may be removed if they satisfy one or more removal criteria based on the attributes of the two best paired minutiae pairs. For example, one constraint may be that if the minutiae index of a particular pair is the same as one of the best-paired minutiae pairs, then that particular pair may be identified as a duplicate within the list of all possible minutiae and removed as a false correspondence. In another example, a rotational constraint may be used to remove particular minutiae pairs that have a large orientation difference compared to the two best-paired minutiae pairs. In another example, distance constraints may be used to keep each particular minutiae pair within the list of all possible mated minutiae geometrically consistent with the two best-paired minutiae pairs. The updated list of minutiae pairs that is generated is the modified list of all possible mated minutiae 712.
After the modified list of all possible mated minutiae is generated, the global consistency pairing module 720 may perform a global consistency pairing operation on the modified list of all possible mated minutiae 722 to generate the list of geometrically-consistent groups 726 (or “globally aligned mated minutiae”). For instance, the global consistency pairing module 720 may initially sort the list modified list of all possible mated minutiae 712 by the similarity score, and then scan the list and remove particular minutiae pairs 724 that have minutiae indexes that are similar to the minutiae pairs with the highest similarity scores in the sorted modified list of all possible mated minutiae 722.
The global consistency pairing module 720 may then initialize a set of groups based on the number of reference minutiae included within the list. For instance, a group may be created for each reference minutia such that if there are multiple minutiae pairs within the list of sorted modified list of all possible minutiae 722 for a single reference minutiae, the multiple minutiae pairs are included in the same group. In some instances, the global consistency pairing module 720 may additionally check the geometric consistency between each of the minutiae pairs within the same group and remove minutiae pairs that are determined not be geometrically consistent. The global consistency pairing module 720 may then compute an average similarity score for each group based on aggregating the individual similarity scores associated with each of the minutiae pairs within the group.
After computing the average similarity scores for each group, the global consistency pairing module 720 may then compare the average similarity scores between each group and select the group that has the highest average similarity score and then provide the list of minutiae that are included within the group for output of the process 700 and include the top average similarity score.
As describe above, FIGS. 5-7 illustrate processes that are utilized by the automatic fingerprint identification system to process, analyze, and match individual minutiae from a search fingerprint to a reference fingerprint. As described in FIGS. 8A-8B, these processes may be utilized within a matching operation for computing derived virtual quality parameters for a search fingerprint.

Derived Virtual Quality Parameters

In general, generating a set of derived virtual quality parameters enables a quality estimation of a search fingerprint using only minutia information associated with a list of minutiae extracted from the search fingerprint. For instance, a minutia quality and a fingerprint image quality may be estimated for a search fingerprint to improve the matching accuracy with respect to receiver operation characteristics associated with a fingerprint matching operation between the search fingerprint and a reference fingerprint. In some examples, the minutia quality and the fingerprint image quality may be used to adjust a computed final similarity score between the search fingerprint and the reference fingerprint after performing fingerprint alignment as described with respect to FIG. 6.
The minutia quality confidence represents a likelihood that minutia information associated with a particular minutia may contribute to generating an accurate final similarity score between a search fingerprint and a reference fingerprint. The minutia quality confidence may be computed based on the minutia density associated with a minutiae density of an OFV. For instance, because minutiae are more likely to be falsely detected when minutiae are closely clustered within a particular region of the fingerprint, calculated distances between a minutia, used as a reference point, and its nearest neighboring minutiae may be used to compute the minutia quality confidence. In addition, the minutia quality confidence may also be computed based on the direction difference between a center minutia and its neighboring minutiae in fingerprint regions other than those where the fingerprint has smooth ridge flow patterns. For instance, in core/delta regions or other noisy regions, the minutia directions are often subject to errors due to directional smoothing. In such regions, a medium quality confidence may be assigned without any computation to prevent errors in the minutia quality confidence based on the errors due to directional smoothing.
The fingerprint quality confidence is an aggregate score of all of individual minutia quality scores for each minutia included within a list of minutiae that may be extracted from a search fingerprint. For instance, the fingerprint quality confidence may computed based on combining the values of the minutia quality confidences. In this regard, the fingerprint quality confidence may be used to represent an overall fingerprint quality during a matching operation between a search fingerprint and a reference fingerprint. In some instances, the fingerprint quality confidence may be used to adjust the value of a computed final similarity score between the reference fingerprint and the reference fingerprint. For example, if the fingerprint quality confidence indicates that the search fingerprint is low quality, the fingerprint quality confidence may be used to reduce the value of the final similarity score to reduce the probability of generating a false match due to quality of the search fingerprint.
FIGS. 8A-8B illustrate exemplary processes for generating and using derived virtual quality parameters such as the minutia quality confidence and the fingerprint quality confidence in fingerprint matching operations. Referring to FIG. 8A, an exemplary process 800 may include computing an image quality score for a search fingerprint. Briefly, a list of minutiae 802 a may be extracted from a search fingerprint, and a list of search OFVs 804 a that includes a respective OFV for each minutia included in the list of minutiae 802 a may generated.
An OFV analysis module 810 may receive the list of search OFVs 804 a and identify a plurality of neighboring minutiae for each minutia included within the list of minutiae 802 a, and generate a list of neighboring minutiae 812. For instance, as shown in the list of neighboring minutiae 812 in FIG. 8A, in some implementations, the OFV analysis module 810 may identify the eight closest neighboring minutiae to each minutia. In other implementations, the OFV analysis module 810 may identify a different number of neighboring minutiae.
The OFV analysis module 810 may additionally analyze the minutia density of the plurality of neighboring minutiae. For instance, the OFV analysis module 810 may compute a direction difference between each minutia and each of the plurality of neighboring minutiae (Δθ), a minimum distance of the nearest neighboring minutiae or neighboring minutiae (d_min), and/or a total number of neighboring minutiae (N_T). In some instances, the OFV analysis module 810 may also compute a maximum distance between the each minutia and all of the neighboring minutia (not shown).
The OFV analysis module 810 may also classify each of the neighboring minutiae as being located within either a close neighborhood (N_C) or a faraway neighborhood (N_F) based on comparing the distance between a particular minutia and each of its neighboring minutiae to a threshold value. For example, neighboring minutiae that have a distance that is lower than the threshold value may be classified as being located within a close neighborhood, whereas neighboring minutiae that have a distance that is greater than a threshold value may be classified as being located within a faraway neighborhood.
After generating the list of neighboring minutiae 812, a minutia quality estimation module 820 may assign a minutia quality confidence (Q) to each minutia included in the list of search minutiae 802 a. For instance, the quality confidences may be a set of finite values that are assigned to each minutia based on the total number of neighboring minutiae, the minimum distance, the number of minutiae that are classified as being located within a close neighborhood, and the number of minutiae that are classified as being located within a faraway neighborhood. The minutia quality estimation module 820 may then generate a list of minutiae quality confidences 822, which is provided to a fingerprint quality estimation module 830 for computing a fingerprint quality confidence.
In some implementations, the minutia quality confidences may be assigned from to the minutiae from a list of five values. For instance, the minutia quality may range from 0 to 4.
The fingerprint quality estimation module 830 may compute the fingerprint quality confidence based on aggregating the individual quality confidences within the list of minutiae quality confidences 822. For example, in some instances, the fingerprint quality estimation module 830 may initially sum all of the individual quality confidences between a particular minutia and each of the nearest neighboring minutiae, and then sum all of the quality confidences for each particular minutia included in the list of search minutiae 802 a. The fingerprint quality estimation module 830 may then provide the fingerprint quality confidence as an output of the process 800.
Referring to FIG. 8B, an exemplary process 850 may include adjusting a similarity score between a search fingerprint and a reference fingerprint based on derived virtual quality parameters. For instance, the a similarity module 860 may receive a pair of aligned fingerprints 852 that may be aligned using the alignment procedure as described previously with respect to FIG. 6. The similarity module 860 may also receive a baseline similarity score (S_B) 854 between the pair of aligned fingerprints 852. The baseline similarity score 854 may be computed using a similarity determination technique described previously with respect to FIG. 5.
After receiving the pair of aligned fingerprints 852 and the baseline similarity score 854, the similarity module 860 may initially calculate the area of an overlapping region (O) 862 between the reference fingerprint and the search fingerprint within the pair of aligned fingerprints 852. The similarity module 860 may also compute the area of the reference fingerprint that is not included in the overlapping region (O_R) and the area of the search fingerprint that is not included in the overlapping region (O_S).
The similarity module 860 may then classify the minutiae within the search fingerprint based on O, O_R, and O_Sinto a set of categories 864. For instance, the categories 864 may include a mated minutiae quality group that indicates a similarity between the fingerprint and the reference fingerprint, a non-mated minutiae quality group that indicates that a particular minutia within the fingerprint has been identified to have a close minutiae, from the other fingerprint, within the overlapping region, and a second non-mated minutiae quality group that indicates that a particular minutia within the fingerprint has been identified to not have a close minutia, from the other fingerprint, within the overlapping region.
The mated minutia quality group may include minutiae within the search fingerprint that have been identified to have a corresponding minutia within the reference fingerprint. Since the mated minutiae indicate a correspondence between the search fingerprint and the reference fingerprint, the minutiae included in the mated minutiae quality group positively contribute to the similarity score adjustment by the similarity module 860.
The non-mated minutiae quality group inside the overlapping region may include minutiae within the search or reference fingerprint that have a close minutia from the other print within the overlapping region. The close minutia was not detected as a mated minutia since the close minutia is outside of a matched threshold, but it is identified as being not too far away from the minutia. Since the non-mated minutiae indicate a non-correspondence, and hence dissimilarity, between the search fingerprint and the reference fingerprint, the minutiae included in the non-mated minutiae quality group that is close to the overlapping region negatively contribute to the similarity score adjustment by the similarity module 860.
The non-mated minutiae quality group inside the overlapping region (also known as “singly non-mated minutiae”) may include minutiae within the fingerprint that do not have close minutiae as described in the previous paragraph within the overlapping region . Since the non-mated minutiae indicate a significant non-correspondence between the search fingerprint and the reference fingerprint, the minutiae included in the non-mated minutiae quality group inside the overlapping region that do not have close minutiae from the other print negatively contribute to the similarity score adjustment by the similarity module 860. In some instances, this group also reduces the similarity score adjustment at a greater magnitude relative to the non-mated minutiae quality group that is close to the overlapping region.
After initially classifying each of the minutiae within the search fingerprint, the similarity module 860 may then count the number of minutiae 866 that are included in each class. The similarity module 860 may then adjust the value of the baseline similarity score based on the number of minutiae within each class and generate an adjusted similarity score 868. For instance, in some implementations, the baseline similarity score 854 may be adjusted based on a particular positive weight associated with the total number of mated minutiae within the mated minutiae quality group, and particular negative weights associated with the respective non-mated minutiae groups. For example, the baseline similarity score may be increased by a larger weight if there are a greater number of mated minutiae identified. Conversely, the baseline similarity score maybe reduced by a larger weight if there are a larger number of non-mated minutiae.
FIG. 9 is an exemplary process 900 for generating derived virtual quality parameters for fingerprint matching. Briefly, the process 900 may include receiving a list of minutiae (910), generating an octant feature vector for each minutia (920), identifying one or more neighboring minutiae (930), computing a minutia quality confidence score (950), computing a fingerprint quality confidence (960), and providing the fingerprint quality confidence for output (970).
In more detail, the process 900 may include receiving a list of minutiae (910). For instance, the automatic fingerprint identification system 100 may receive the list of search minutiae 802 a extracted from a search fingerprint.
The process 900 may include generating an octant feature vector for each minutia (920). For instance, the automatic fingerprint identification system 100 may generate the list of search OFVs 804 a for the list of search minutiae 802 a.
The process 900 may include identifying one or more neighboring minutiae (930). For instance, the OFV analysis module 810 may identify one or more neighboring minutiae within a particular octant neighborhood for the OFV for each minutia included in the list of search minutiae 802 a.
The process 900 may include computing, for each minutia, a direction difference between each minutia and neighboring minutia (940). For instance, the OFV analysis module 810 may compute, for each minutia included in the list of search minutiae 802 a, a direction difference between each of the minutia included in the list of search minutiae 802 a, and each of the one or more neighboring minutiae identified for the OFV for each minutia included in the list of search minutiae 802 a. As shown in FIG. 8, the list of neighboring minutiae 812 may include the respective direction differences between a particular minutia and each of the one or more neighboring minutiae.
The process 900 may include computing a minutia quality confidence score (950). For instance, the minutia quality estimation module 820 may compute, for each minutia included in the list of search minutiae 804 a, a minutia quality confidence based at least on one or more parameters. For example, as shown in the list of neighboring minutiae 812, the one or more parameters may include the number of neighboring minutiae, the minimum distance, the number of neighboring minutiae identified as being located within a close neighborhood, or a number of neighboring minutiae identified as being located within a faraway neighborhood.
The process 900 may include computing a fingerprint quality confidence (960). For instance, the fingerprint quality estimation module 830 may compute the fingerprint quality confidence based at least on the value of an aggregate minutiae quality confidence for each minutia included in the list of search minutiae 802 a, and a number of minutiae within the list of search minutiae 802 a that are identified to have a sufficient number of neighboring minutiae. For example, the aggregate minutiae quality confidence for each minutia included in the list of search minutiae 802 a may represent a combination of the respective minutiae quality confidences for a single minutia and each of the one or more neighboring minutiae identified for the octant feature vector for the single minutia
The process 900 may include providing the fingerprint quality confidence for output (970). For instance, the fingerprint quality estimation module 830 may provide the fingerprint quality confidence for output to the automatic fingerprint identification system 100.
It should be understood that processor as used herein means one or more processing units (e.g., in a multi-core configuration). The term processing unit, as used herein, refers to microprocessors, microcontrollers, reduced instruction set circuits (RISC), application specific integrated circuits (ASIC), logic circuits, and any other circuit or device capable of executing instructions to perform functions described herein.
It should be understood that references to memory mean one or more devices operable to enable information such as processor-executable instructions and/or other data to be stored and/or retrieved. Memory may include one or more computer readable media, such as, without limitation, hard disk storage, optical drive/disk storage, removable disk storage, flash memory, non-volatile memory, ROM, EEPROM, random access memory (RAM), and the like.
Additionally, it should be understood that communicatively coupled components may be in communication through being integrated on the same printed circuit board (PCB), in communication through a bus, through shared memory, through a wired or wireless data communication network, and/or other means of data communication. Additionally, it should be understood that data communication networks referred to herein may be implemented using Transport Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), or the like, and the underlying connections may comprise wired connections and corresponding protocols, for example, Institute of Electrical and Electronics Engineers (IEEE) 802.3 and/or wireless connections and associated protocols, for example, an IEEE 802.11 protocol, an IEEE 802.15 protocol, and/or an IEEE 802.16 protocol.
A technical effect of systems and methods described herein includes at least one of: (a) increased accuracy in facial matching systems; (b) reduction of false accept rate (FAR) in facial matching; (c) increased speed of facial matching.
Although specific features of various implementations of the invention may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the invention, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing.
This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

Claims

What is claimed is:

1. A method comprising:

obtaining data indicating (i) a list of minutiae extracted from a fingerprint image, and (ii) for each minutia included in the list of minutiae, an octant feature vector that identifies one or more neighboring minutiae within the fingerprint image;

for each minutia included in the list of minutiae:

computing one or more parameters based on comparing features of a minutia and respective features of one or more neighboring minutiae for the minutia, and

computing an aggregate minutia quality confidence score based on combining the one or more computed parameters;

computing a fingerprint quality confidence score for the fingerprint image based at least on combining the aggregate minutia quality confidence scores; and

providing the fingerprint quality confidence scores for output.

2. The method of claim 1, wherein:

combining the aggregate minutia quality confidence scores comprises determining a number of minutiae within the list of minutiae having a number of neighboring minutiae that satisfies a threshold; and

the fingerprint quality confidence score is computed based at least on the determined number of minutiae within the list of minutiae having a number of neighboring minutiae that satisfies a threshold.

3. The method of claim 1, wherein the one or more parameters comprise at least one of:

a calculated distance difference between each minutia included in the list of minutiae and one or more neighboring minutiae for each minutia;

a number of neighbor minutiae for each minutia included in the list of minutiae;

a number of minutiae included in the list of minutiae that are located inside a close radius threshold; or

a number of minutiae included in the list of minutiae that are located outside a far radius threshold.

4. The method of claim 1, further comprising:

computing a fingerprint similarity score between the fingerprint image and a reference fingerprint image based at least on a value of the fingerprint quality confidence score.

5. The method of claim 4, wherein the fingerprint similarity score between the fingerprint image and the reference fingerprint image is computed additionally based on (i) a number of minutiae within the list of minutiae that are identified as mated minutiae, and (ii) a number of minutiae within the list of minutiae that are identified as non-mated minutiae.

6. The method of claim 5, further comprising:

computing a match quality confidence score based on the number of minutiae within the list of minutiae that are identified as mated minutiae;

computing a non-match minutiae quality confidence score based on the number of minutiae within the list of minutiae that are identified as non-mated minutiae; and

wherein the fingerprint similarity score is adjusted based at least on (i) a value of the match quality confidence score, and (ii) a value of the non-match minutia quality confidence score.

7. The method of claim 6, wherein adjusting the value of the fingerprint similarity score comprises:

computing at least an area within the fingerprint image that corresponds to an overlapping region between the fingerprint image and the reference fingerprint image;

classifying, based at least on the computed area within the fingerprint image, each of the minutiae included in the list of minutiae to one or more quality indicative groups; and

adjusting a value of the fingerprint similarity score based at least on a number of minutiae classified as each of the one or more quality indicative groups.

8. The method of claim 7, wherein the one or more quality indicative groups comprises a mated minutiae quality group and a non-mated minutiae quality group.

9. A system comprising:

one or more computers; and

one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:

for each minutia included in the list of minutiae:

providing the fingerprint quality confidence scores for output.

10. The system of claim 9, wherein:

11. The system of claim 9, wherein the one or more parameters comprise at least one of:

12. The system of claim 9, further comprising:

13. The system of claim 12, wherein the fingerprint similarity score between the fingerprint image and the reference fingerprint image is computed additionally based on (i) a number of minutiae within the list of minutiae that are identified as mated minutiae, and (ii) a number of minutiae within the list of minutiae that are identified as non-mated minutiae.

14. The system of claim 13, wherein the operations further comprise:

15. The system of claim 14, wherein adjusting the value of the fingerprint similarity score comprises:

16. One or more non-transitory computer-readable media storing instructions that, when executed by one or more computers of a server system, cause the server system to perform operations comprising:

for each minutia included in the list of minutiae:

providing the fingerprint quality confidence scores for output.

17. The one or more non-transitory computer-readable media of claim 16, wherein:

18. The one or more non-transitory computer-readable media of claim 16, wherein the one or more parameters comprise at least one of:

19. The one or more non-transitory computer-readable media of claim 16, further comprising:

20. The one or more non-transitory computer-readable media of claim 19, wherein the fingerprint similarity score between the fingerprint image and the reference fingerprint image is computed additionally based on (i) a number of minutiae within the list of minutiae that are identified as mated minutiae, and (ii) a number of minutiae within the list of minutiae that are identified as non-mated minutiae.