CN107909031A - A kind of scene of a crime fingerprint ridge leaves region frequency dynamic reconstruction method - Google Patents
A kind of scene of a crime fingerprint ridge leaves region frequency dynamic reconstruction method Download PDFInfo
- Publication number
- CN107909031A CN107909031A CN201711128381.1A CN201711128381A CN107909031A CN 107909031 A CN107909031 A CN 107909031A CN 201711128381 A CN201711128381 A CN 201711128381A CN 107909031 A CN107909031 A CN 107909031A
- Authority
- CN
- China
- Prior art keywords
- fingerprint
- streakline
- data
- region
- site
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/13—Sensors therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/34—Smoothing or thinning of the pattern; Morphological operations; Skeletonisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
- G06V10/993—Evaluation of the quality of the acquired pattern
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1347—Preprocessing; Feature extraction
Abstract
The present invention relates to a kind of scene of a crime fingerprint ridge to leave region frequency dynamic reconstruction method, comprises the following steps：Fingerprint on site streakline overlay area circumference line is identified and data are extracted；Fingerprint on site streakline is left region on than middle stamp fingerprint to be rebuild；Carry out the dynamic reconstruction that scene of a crime fingerprint ridge leaves region frequency.Present invention is mainly used for the ultra-large system of fingerprints of more alignment algorithm frameworks, it can effectively lift that system of fingerprints builds the image quality level in storehouse and system of fingerprints is solved a case efficiency, and there is protrusion meaning in " system of fingerprints compares under-enumeration risk analysis ", " locking of under-enumeration target zone ", " all kinds of plane stamp fingerprint image data forensic science field application in actual combat feasibilities evaluation and tests " etc..
Description
Technical field
The present invention propose it is a kind of based on " big data excavation " and " pattern-recognition " technology " scene of a crime fingerprint leaves area
Domain frequency " dynamic reconstruction algorithm model, belongs to living things feature recognition field.
Background technology
Automatic system of fingerprint recognition (AFIS) is the most widely used personal feature identifying system in the world today, is both various countries
The police assert that case perpetrator and great cases are murdered the preferred sharp weapon of personnel identity, and the police are to investigating object, unknown
The identity of name corpse, special industry practitioner, person limited in disposing capacity, the personnel that wander away, fugitive suspect, key-point management object etc.
Prefered method that is quick, accurately assert is carried out, its huge solve a case efficiency and fast reaction, precisely direct human identifying, strike
The characteristics of also often spoken approvingly of for mechanism of handling a case of various countries.
Ten finger fingerprint databases build storehouse quality, and particularly the quality of fingerprint image data plays straight with the system level of solving a case
Connect correlation.Stamp region is not complete, the finger print data of streakline lack of defination, characteristic reaction difference storage, target candidate can be caused to refer to
Line row's antecedent reduces, significantly improves omission factor and increase mutual authentication difficulty, or even misleads fingerprint expert misattribution an innocent person people
Member or error exception personnel concerning the case.In addition, large quantities of low quality data storages, system can be caused, which to compare feedback speed, to be reduced, preceding
Platform period of reservation of number lengthens, and influences the enthusiasm that a collimation method front yard Scientific Organizations carry out finger print information application in actual combat.
The characteristics of for a certain particular brand AFIS system alignment algorithms, take into account the enterprise schema of certain class capturing service, if
It is not originally problem (the foreground acquisition workstation of nearly all AFIS systems and rear number of units that meter ten, which refers to fingerprint image quality control algolithm,
There is such function according to storehouse).But the current police of various countries have to the quality control models of existing AFIS systems with per family more it is discontented,
" system foreground quality control module " collection finger print data is closed also as universal phenomenon in the industry.The police of various countries collector
Complaint be concentrated mainly on：It is all " black box module " that all AFIS manufacturers, which provide the quality assessment program of equipment and evaluation and test algorithm,
Evaluation mechanism is opaque, and actual efficiency can not also be calculated, and the judgement of machine is believed with it, not as believing the micro-judgment of oneself.
In addition, for many years, the fingerprint material evidence of forensic science department of various countries compare brainstrust above-mentioned fingerprint quality is evaluated it is " black
Tank module " also more complaint, is concentrated mainly on：The quality assessment algorithm of existing fingerprint manufacturer, mainly based on " guarantee fingerprint
Archival image it is comprehensive " (ensuring that ten finger fingerprints each refer to each position streakline of image of position and gathered) and " improve
The accuracy of archival image streakline " (ensures that picture quality reaches the minimum standard of machine characteristic identification, improves target folder and exist
Compare row's antecedent in short-list).Using such standard as ten qos thresholds for referring to fingerprint collecting work, not only in " line
The comprehensive index of line collection " aspect is far beyond " level that most of collectors can reach " (in real work, it is difficult to protect
Whole streaklines of ten finger positions of card suspect can be gathered) and " actual combat of fingerprint comparison work needs " (in real work,
No matter machine compares or mutual authentication, the main points of concern are all that " target suspect refers to position in scene of a crime left fingerprints material evidence
The image quality level of upper corresponding region ridge alignment picture ".And influenced by factors such as human finger's structure and motor habits, not only
Ten finger fingerprints respectively refer to frequency difference of the position in scene of a crime legacy field fingerprint, and each refer to position different parts region streakline
Leaving frequency, there is also very big difference.Those " the highest finger positions of frequency of use " and " most frequent contact object in situ surface, and
Leave the finger areas of streakline in scene ", it is only the position that the control of fingerprint file picture quality ought to be paid close attention to), and these areas
The fingerprint image quality level in domain is only the main points of fingerprint quality control.In other words, existing AFIS systems mainstream quality assessment is calculated
" comprehensive, the high-quality collection standard " that method is pursued, gathers between department and the actual demand of forensic science department with the police, exists very
Big disconnection.
Therefore, forensic science industry needs to find the dynamic reconstruction skill that a kind of scene of a crime fingerprint ridge leaves region frequency
Art, refers in fingerprint image quality evaluation and test " same finger position different zones streakline picture quality " for ten and " same people ten refers to fingerprint
Difference refers to a streakline picture quality " horizontal real-time, accurate assign is provided to ten finger fingerprint total qualities grading contribution calculations weigh
Parameter model, so as to solve existing system of fingerprints fingerprint image quality evaluation and test algorithm, " each region streakline acquisition quality is to each finger position
Fingerprint image quality grading tax weight parameter " and " respectively refer to position quality weight and assign weight parameter to ten finger fingerprint total qualities gradings " are without system
Meter learns the problem of data supporting.
The content of the invention
To solve the above problems, this method is based on " big data excavation " and " pattern-recognition " technology, establishes and a set of be intended to move
State rebuilds the method that scene of a crime fingerprint ridge leaves region frequency.
The technical solution adopted by the present invention to solve the technical problems is：A kind of scene of a crime fingerprint ridge leaves region frequency
Dynamic reconstruction method is spent, is comprised the following steps：
Fingerprint on site streakline overlay area circumference line is identified and data are extracted；
Fingerprint on site streakline is left region on than middle stamp fingerprint to be rebuild；
Carry out the dynamic reconstruction that scene of a crime fingerprint ridge leaves region frequency.
It is described fingerprint on site streakline overlay area circumference line is identified and data extraction comprise the following steps：
Step S02.1, extraction fingerprint on site leaves streakline and is converted into structuring streakline data Sgql；
Step S02.2, extraction and storage organization streakline dataControl point, Cgql curves；
Step S02.3, fingerprint on site streakline overlay area circumference line number evidence is extracted.
The step S02.1 comprises the following steps：
(1) picture noise is removed to fingerprint on site image Pql and obtains fingerprint on site enhancing figure Peql；
(2) prospect streakline is extracted, and region segmentation is carried out according to Quality estimation, excludes engineer's scale lines；
(3) extraction and calibration of the field of direction：First ask for removing the Peql field of directions of engineer's scale and do flat using gradient algorithm
Sliding processing, further according to Qrql values, by the direction field data in Qrql high level region in adjacent masses region to Qrql low values region
Direction field data is covered, and obtains fingerprint on site general direction field data Dql；
(4) calculating of ridge frequency：The direction field parameters provided using Dql, along Peql streakline direction projections；Measure one-dimensional
The extreme point of perspective view, obtains the frequency of extreme point, is ridge frequency Frql；
(5) Gabor filtering algorithms are used, streakline sharpening processing is carried out to Peql by parameter of Dql and Frql, and will be disconnected
The streakline opened reconnects, and obtains the Gabor filter result data Pgql of Peql；
(6) binaryzation：Binaryzation is carried out to Pgql and obtains bianry image Bgql；
(7) streakline refines：Bgql is changed into the refinement figure of setting pixel wide, i.e. ridge alignment by Hilditch algorithms
Tgql；
(8) tracking and data storage of streakline are refined：Detect whole streakline endpoint in Tgql, then using whole endpoints as
Starting point, tracks each streak line of Tgql one by one, will form the coordinate of each pixel of streakline according to Ridge following order
Store, that is, obtain the structuring streakline data Sgql of Tgql.
Described (2) extract prospect streakline, and carry out region segmentation according to Quality estimation, exclude engineer's scale lines including following
Step：
A) orientation consistency algorithm process Peql each points are used, extraction has the regular i.e. orientation consistency parameter in direction to be located at
Texture region in setting range, region Rrql is left as fingerprint ridge, and fingerprint ridge is left its beyond the Rrql of region
His region is as the background area Riql unrelated with fingerprint ridge；
B) threshold value of connectivity analysis algorithm and the threshold value of orientation consistency algorithm are set, according to above-mentioned two algorithms amendment
Rrql and Riql data, form continuous distribution；
C) image quality index of Rrql pixel resolutions, contrast and contrast is obtained, by these index weighted sums, i.e.,
Obtain the comprehensive quality index Qrql of each pixel comprehensive qualities of reflection prospect Prql；Prql is divided into according to Qrql numerical value some
Quality region Pgql；
D) detect in Peql with the presence or absence of a plurality of straightway, that is, engineer's scale of parallel equidistant arrangement；As existed, then by these
Region labeling where parallel equidistant straightway is engineer's scale region, and removes engineer's scale.
The step S02.2 comprises the following steps；
(1) definition structure streakline dataControl point structure, Cgql curvilinear structures；
(2) extract and storeControl point and Cgql curves；
Sgql data are called, since the streakline starting point of left side, untill the streakline terminating point of right side, are extracted every distance d
Certain point is used as control point on streaklineDefining starting point and right side terminating point on the left of streakline at the same time is
First will all it extractIt is stored in by control point structure in relevant database SDB1, then Sgql data is pressed
It is stored according to Cgql curvilinear structures in relevant database SDB1.
Step S02.3 comprises the following steps：
(1) is establishedModel：Establish and be used to reflectPoint existence dataMatrix model；
(2) settings scanning sliding window；
(3) establishes fingerprint on site MDOCgql models, for storing DOCgql data distribution range datas；
(4) is extracted and storage fingerprint on site leaves area periphery contour line control point
Z-type scanning is carried out to MDOCgql wholes cell data using scanning sliding window；Often row scanning result is most to record
Left sideScanning element coordinate and the rightmost sideScanning element coordinate, and be denoted asAs scan line withoutUnit, then jump to next line；If scan line only has 1Unit, then also record the seat of the unit
It is designated asCoordinate；
, will be all after completing all row scanningsThe x values and y values of coordinate data are multiplied by 8 and obtain a little respectively
Then Pql images are mapped that to(8x, 8y)；
Since y value minimum points, connected one by one along clock-wise order allForm DOCgql；
Will be all above-mentionedCoordinate is stored into database.
It is described fingerprint on site streakline left into region carry out reconstruction on than middle stamp fingerprint comprise the following steps：
Step S03.1, optimal details matching reference coordinate is asked for reference to data Tm；
Step S03.2, analysis is carried out to Tm the optimal rigid motion that fingerprint on site streakline leaves regional reconstruction is calculated
Relation M；
Step S03.3 is mapped to the streakline corresponding region on its correspondence Pkt image using M as parameter, by DOCgql data, obtains
Obtain circumference and rebuild data.
Step S03.1 comprises the following steps：
(1) defines the data structure and parameter that fingerprint on site-stamp fingerprint minutiae matching assigns power bigraph (bipartite graph) Model B Gltm：
Establish array minu pMnt [N] and represent all details, N is the number of details；In bigraph (bipartite graph)
One details of each vertex representation；Define that two-dimensional array pW [M] [N] represents each details in bigraph (bipartite graph)
With weight；
(2) assigns the modeling of power bigraph (bipartite graph)
Represent that each pair between the characteristic Mql of fingerprint on site and the characteristic Mkt of stamp fingerprint is matched using IFV
Rotation between minutiae point translates unrelated amountThat is the matched local mode of minutia；
Centered on Pql details i, amount IFVi unrelated with the details construction rotation translation that distance i is nearest；
It is unrelated with the details construction rotation translation that distance j is nearest centered on the details j on Pkt with i Corresponding matchings
Measure IFVj；
Matching similarity between IFVi and IFVj
In above formula, qiThe picture quality Qrql, q of coordinate position where details ijSat where details j
The picture quality Qrql of cursor position；
Using full details characteristic point on Pql and Pkt as vertex, wijFor weight, construction assigns power bigraph (bipartite graph)；
(3) is directed to BGltm models, and data Tm is drawn using Kuhn-Munkres algorithms.
Step S03.2 comprises the following steps：
Matched for the details in Tm to pij, under the conditions of rigid body translation, using Pql and Pkt coordinate origins as rotation
Turn center, the rotation angle T of i, jθMeet following relation：
(x, y) and (x', y') represents matching to coordinate, T respectivelyxAnd TyRepresent translation distance, TθRepresent rotation angle；
Calculate rigid motion parameter be：
Tx=xj-(xicosTθ-yisinTθ)
TyyjxisinTθ+yicosTθ)
Tθ=θj-θi
Define Mij=(Tx,Ty,Tθ) be i to j kinematic parameter；
Calculate to obtain and be possible to set of matches { pijMotion parameter set { Mij, average, obtain optimal rigid body fortune
Dynamic relation M=(Tx,Ty,Tθ), pijRepresent possible match point.
The dynamic reconstruction that the progress scene of a crime fingerprint ridge leaves region frequency comprises the following steps：
S04.01, establish DRR templates：The structure of DRR templates is ten square chart pictures, corresponds to ten respectively and refers to respectively referring to for fingerprint
Position；
Whole grids are subjected to subregion according to grid Md, whole Md are encoded according to position；
S04.02, the region frequency reconstruction for carrying out DRR, obtain scene of a crime fingerprint ridge and leave region frequency dynamic reconstruction
Data Fdrr；
(1), the extraction and conversion of DRR area datas
DRR data are scanned using scanning sliding window, record fingerprint on site streakline is left corresponding to reconstruction regions
Md coordinate point datas；After the completion of scanning, DRR regions are scaled to the grid data of one group of Md coordinate points；
(2), by whole DRR data reconstructions on each finger position, you can obtain scene of a crime fingerprint ridge and leave region frequency
Dynamic reconstruction data Fdrr；
S04.03, by Fdrr data pass through graphical display.
The invention has the advantages that and advantage：
1st, in the second step of this method, analyzed using image enhancement, region segmentation, field of direction extraction, ridge frequency,
Gabor is filtered, binaryzation, streakline refinement, and Ridge following, streakline flow to consistency desired result, the analysis of streakline continuity, Hough transformation
Isotype identification technology, big data is carried out to the fingerprint on site image of police automatic system of fingerprint recognition memory storage " data in ratio "
Excavate, while ensuring that live left fingerprints streakline automatically extracts objectivity and accuracy, effectively eliminate " the unrelated line of background
Line " and " engineer's scale image " automatically extract the interference brought to fingerprint on site streakline, are accurately extracted that " live streakline leaves region
Circumference data "；
2nd, in this method third step, scene is referred to using " assigning power bigraph (bipartite graph) model " and Kuhn-Munkres algorithms first
The minutia point data of relation map file fingerprint is analyzed in line and its ratio, preferably goes out optimal " reference coordinate reference
Data "；Secondly, fingerprint on site streakline is calculated using the state modulator algorithm of rigid body translation and leaves the " optimal of regional reconstruction
Rigid motion relation data "；Finally, with " optimal rigid motion relation data " for parameter, " live streakline leaves region by foregoing
Circumference data " be mapped to the streakline corresponding region on its map file fingerprint image, obtain objective, accurate " existing
Field streakline leaves area periphery contour reconstruction area data "；
3rd, it is " existing by what is obtained to third step using the statistics matrix model specially designed in the 4th step of this method
Streakline leaves area periphery contour reconstruction area data " frequency statistics, obtain that " scene of a crime fingerprint ridge leaves region frequency
Spend dynamic reconstruction data " Fdrr, for ten refer in fingerprint image qualities evaluation and test " same finger position different zones streakline picture quality " and
" same people ten refers to fingerprint difference and refers to a streakline picture quality " is horizontal to provide " ten refer to fingerprint total quality grading contribution calculation "
In real time, weight parameter model is accurately assigned, so as to solve existing system of fingerprints fingerprint image quality evaluation and test algorithm " each region line
Line acquisition quality assigns weight parameter to each finger position fingerprint image quality grading " and " respectively refer to position quality weight and refer to fingerprint entirety matter to ten
The problem of amount grading tax weight parameter " is supported without statistical data.
4th, in addition, in the 4th step of this method, using specially design " leave frequency in streakline region --- visible spectrum frequency
Rate " transfer algorithm and " leave frequency in streakline region --- 3 D stereo surface chart " transfer algorithm, realize region-wide to each finger position
Leave the mobilism simulation of frequency in streakline scene of a crime.The analogy method and its back-end data, are used directly for all kinds of planes
Stamp fingerprint image data (such as entry and exit, identity card, driver, the registration of examinee's fingerprint) is carried out under battle conditions in forensic science field
The evaluation and test of application feasibility.
5th, present invention is mainly used for the ultra-large system of fingerprints of more alignment algorithm frameworks, it can effectively lift system of fingerprints and build
The image quality level and system of fingerprints in storehouse are solved a case efficiency, and in " system of fingerprints compares under-enumeration risk analysis ", " under-enumeration target model
Enclose locking ", " all kinds of plane stamp fingerprint image datas (such as entry and exit, identity card, driver, the registration of examinee's fingerprint) courts
Scientific domain application in actual combat feasibility evaluation and test " etc. has prominent meaning.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 a are the fingerprint on site image Pql that big data method extracts；
Fig. 2 b are the Sgql design sketch of Pql extractions；
Fig. 3 be fingerprint on site streakline leave region highlight design sketch；
Fig. 4 is the matched local mode schematic diagram of minutia；
Fig. 5 is that fingerprint on site assigns power bigraph (bipartite graph) schematic diagram with it than the details of middle archives fingerprint；
Fig. 6 a are that fingerprint on site streakline leaves area periphery contour reconstruction design sketch one；
Fig. 6 b are that fingerprint on site streakline leaves area periphery contour reconstruction design sketch one；
Fig. 7 is the DRR template schematic diagrames of a certain finger position；
Fig. 8 is ten finger fingerprint Fdrr data " visible ray optical wavelength thermal map " display renderings；
Fig. 9 is a certain finger position Fdrr data " 3 D stereo frequency figure " display renderings.
Embodiment
With reference to embodiment, the present invention is described in further detail.
As shown in Figure 1, this method comprises the following steps：
Step S01：Big data is sampled.
This step needs the information of AFIS databases storage existing to forensic science department to carry out mobilism big data pumping
Sample.The data for needing to extract mainly include：
1st, relation data in fingerprint ratio.The relation data than in, is that fingerprint system user (generally forensic science department) exists
Gradually accumulated in actual combat comparison work, record certain piece of fingerprint on site of certain case scene of a crime extraction, pass through system of fingerprints ratio
Pair and multigroup fingerprint expert (every group of at least two people) independently carries out manually identify, it was demonstrated that this piece of fingerprint on site material evidence and someone
(general is crime suspect) certain refers to the data record that a stamp archives fingerprint has " same relation ".
2nd, forensic science department extracts in scene of a crime, builds storehouse and the fingerprint on site material evidence data than in, the number
According to this piece is both included by the view data (hereinafter referred to as Pql) than middle fingerprint on site, also include (case, material evidence are related) word letter
Cease data and the characteristic (hereinafter referred to as Mql) of this piece of fingerprint on site.
3rd, police (judicial authority) gathers, builds storehouse and ten than in refer to finger print datas, which both included
This piece by it is more a certain than middle stamp archives finger position fingerprint view data (hereinafter referred to as Pkt), also include (by stamp people) word believe
Cease data and the characteristic (hereinafter referred to as Mkt) of this piece of stamp fingerprint.
Step S02：The identification of fingerprint on site streakline overlay area circumference line and data extraction.
This step is needed under the support of mode identification technology, to the fingerprint on site streakline overlay area of foregoing Pql data
Circumference line number is extracted according to progress algorithm automatic identification and coordinate data, its key technology main points is：
What S02.1, fingerprint on site left streakline automatically extracts technology
Being limited by material evidence legacy conditional, the fingerprint ridge clarity on Pql images is often poor, therefore, live streakline
Automatically extract the problem of being one more complicated.For this reason, this step uses image enhancement, image segmentation, field of direction extraction, streakline
Frequency calculates, Gabor filtering, binaryzation, refinement, a variety of processing modes such as Ridge following storage, it is ensured that extraction fingerprint on site line
The objectivity and accuracy of line number evidence.As shown in Fig. 2 a, Fig. 2 b.
The specific method of this step is as follows：
(1) image enhancements
Calculated using " gray scale normalization ", " histogram equalization ", " mean filter ", " filtering at a high speed ", " trend pass filtering " etc.
Method removes picture noise, obtains the enhancing figure Peql of Pql images；
(2) extraction of prospects streakline, Quality estimation, region segmentation and engineer's scale lines exclude
A variety of body surfaces on scene of a crime all can legacy field fingerprint material evidence, shoot extraction scene in explorer and refer to
During print image, the above-mentioned original all kinds of lines of trace body surface (generally process, decorate, using abrasion etc. to be formed) that hold are as carrying on the back
Scape image, together can also be extracted with fingerprint material evidence streakline.In addition, carried according to the fingerprint material evidence image of forensic science industry
Standard is taken, to ensure that the fingerprint on site image scaled size of typing AFIS systems is correct, technical staff's floor extraction scene
During fingerprint, it is necessary to subsidiary 1：1 than row ruler image.Therefore, for objective and accurate extraction fingerprint on site streakline data, it is necessary to above-mentioned
" the false streakline " that " the unrelated streakline of background " and " engineer's scale lines " produces is identified and eliminates, and specific method is：
A) orientation consistency algorithm process Peql each points are used, the texture region that extraction has direction regularity is used as " fingerprint
Streakline leaves region " (hereinafter referred to as Rrql), " noise is serious, the rambling region of orientation consistency " is defined as " with finger
The unrelated background area of line streakline " (hereinafter referred to as Riql)；
Wherein xi,yiIt is the transverse and longitudinal coordinate value of a certain pixel (i, j) in fingerprint image.θijFor the picture element (i, j)
Direction.H, k represents cumulative iteration variable respectively,Expression seeks partial derivative to x,Represent that y seeks partial derivative；rijRepresent direction one
Cause property.
B) the isolated zonule being sporadicly distributed to the part in Rrql and Riql using connectivity analysis algorithm is carried out " preceding
Scape/background attribute judges ", and according to judging result modified R rql and Riql data repeatedly, until Rrql and Riql is formed completely
It is continuously distributed；
C) calculated by orientation consistency, gray average and variance and obtain Rrql pixel resolutions, contrast and contrast etc.
Image quality index, by these index weighted sums, that is, obtain each pixel comprehensive qualities of reflection prospect Prql " comprehensive quality refers to
Mark " (hereinafter referred to as Qrql).According to the height of Qrql numerical value, if Prql is divided into dry mass different zones (hereinafter referred to as
Pgql)；
D) detected using Hough transformation algorithm in Peql with the presence or absence of " a plurality of straightway of parallel equidistant arrangement " (ratio
Ruler), such as exist, be then " engineer's scale region " by the region labeling where these parallel equidistant straightways, and remove engineer's scale.
(3) extraction and calibration of the field of directions
First using gradient algorithm ask remove engineer's scale the Peql field of directions and do smoothing processing, then with Qrql height be according to
According to the field of direction in utilization Qrql low values region that the direction field data in Qrql high level region closes on it (in adjacent masses region)
Data are calibrated (covering), are obtained accurately " fingerprint on site general direction field data " (hereinafter referred to as Dql), the gradient method side of asking
It is as follows to field formula：
Wherein xi,yiIt is the transverse and longitudinal coordinate value of a certain pixel (i, j) in fingerprint image.θijFor the picture element (i, j)
Direction.H, k represents cumulative iteration variable respectively,Represent that x seeks partial derivative,Represent that y seeks partial derivative；rijRepresent that direction is consistent
Property.
(4) calculating of ridge frequencies
The direction field parameters provided using Dql, along Peql streakline direction projections.Measure the extreme point of One Dimensional Projection figure, meter
The frequency for obtaining extreme point is calculated, is ridge frequency (hereinafter referred to as Frql)；
(5) .Gabor is filtered
Using Gabor filtering algorithms, using Dql and Frql as parameter, streakline sharpening processing is carried out to Peql, and will be switched off
Streakline reconnect, obtain the Gabor filter results data (hereinafter referred to as Pgql) of Peql；
(6) binaryzations
Binary conversion treatment is carried out to Pgql, if gray threshold is T, by the gray scale of pixel of the gray value in Pgql more than T
Value is set to 0 (representing non-streakline), and gray value in Pgql is set to 1 (expression streakline) less than or equal to T, so that Pgql is changed into 0,
1 bianry image (hereinafter referred to as Bgql)；
(7) streaklines refine
Bgql is changed into the refinement figure of 1 pixel wide, i.e. ridge alignment by Hilditch algorithms (hereinafter referred to as
Tgql)；
(8) refines the tracking and data storage of streakline
Streakline endpoint whole in Tgql is detected, then using whole endpoints as starting point, tracks each of Tgql one by one
Streakline, the coordinate for each pixel for forming streakline is got up according to Ridge following sequential storage, that is, obtains the structuring of Tgql
Streakline data Sgql.
S02.2, Sgql control pointThe extraction and storage of Cgql curves
The Sgql information content that Rrql is calculated is very big, it has not been convenient to the localization process of storage and lower step.Therefore, this step makes
Defined with the structure of special design, be extracted the control point of Sgql dataWith Cgql curves, and relationship type is stored it in
It is spare in database SDB1.The specific method of this step is as follows：
(1) .Sgql control pointsStructure definition, Cgql curves ask method and its structure to define
Sgql control pointsThe structure of data is defined as：
typedef struct tagGFRT_SplineCtrlPoint
{
double fx；// control point x coordinate
double fy；// control point y-coordinate
}GFRT_SPLINECTRLPOINT；// control point structure
(control point x, the method for expressing of y-coordinate is with reference to Chinese people republic industry standards of public safety《Characteristic point is with referring to
Line direction coordinate representation method》Fingerprint Minutia Coordination and Direction System GA-
Minutia position coordinates architectural definition in 775 (2008), hereinafter referred to as Cc).
Cgql curves ask the method formula to be：
Ni,0(u) i-th 0 time B- spline base function, N are representedi,p(u) i-th p times B- spline base function, u are representediRepresent
The component value of i-th of knot vector, p represent the number of basic function, and u represents knot vector, and i represents node ui in knot vector u
In order.
The structure of Cgql curve datas is defined as：
(2) .Sgql control pointsWith the extraction and storage of Cgql curves
The Sgql data for calling S02.1 steps to obtain, since the streakline starting point of left side, are to right side streakline terminating point
Only, every distanceControl point is used as on extraction refinement streakline (hereinafter referred to as at 1 point), while define thin
Changing starting point and right side terminating point on the left of streakline is
First will all it extractThe control point data structure storage defined according to S02.2. (1) step is in relationship type
In database SDB1, then the Cgql data structure storages that Sgql data are defined according to S02.2. (1) step are in relational data
In the SDB1 of storehouse.
The extraction of S02.3, fingerprint on site streakline overlay area circumference line number evidence
" fingerprint on site streakline overlay area circumference line number evidence " (hereinafter referred to as DOCgql), refers to that " scene refers to for simulation
Line streakline overlay area circumference line " (hereinafter referred to as Orql) distribution space position " fingerprint on site leaves area periphery wheel
Profile control point " is (hereinafter referred to as) corresponding pixel is (hereinafter referred to as on Pql fingerprint on site images) seat
Mark data.
(1) establishes MCp models
Model refers to specially design, for establish reflection "Point existence data " are (hereinafter referred to asData)
Matrix model, its data structure and assignment method are：
For the matrix of 512*512 units.The each element of the matrix is 2 bits, for storing(hereinafter referred to as).It is right with reference to the definition of Cc coordinate-systemsWhole unitsCarry out assignment：Coordinate corresponding unitIt is assigned a value of
01, it is non-Coordinate corresponding unitIt is assigned a value of 00；
(2) scans the parameter designing of sliding window
In view of between Cgql curves there are larger gap, so, scanning sliding window it is too small, fingerprint on site line can be caused
The continuity of the scanning result data (hereinafter referred to as DOCgql) of line overlay area circumference line (hereinafter referred to as OCgql)
Reduce；And it is excessive to scan sliding window, can cause DOCgql data distortions.By verifying repeatedly, 8 element *, 8 elements are employed,
Step value is more moderate for the sliding window of 8 elements.
(3) establishes fingerprint on site MDOCgql models
MDOCgql models, refer to specially design, for storing the matrix model of DOCgql data distribution range datas, its
Data structure and assignment method are：
The matrix of a 64*64 unit is established, each element is 2 bits.It is former from coordinate with reference to the definition of Cc coordinate-systems
Point starts, rightModel data carry out Z-type scan process, take current sliding window mouthMaximum inputs MDOCgql moulds
Type；
(4).Extraction and storage
With reference to the definition of Cc coordinate-systems, using 1*1 units, step value is 1 scanning sliding window, whole to MDOCgql
Cell data carries out Z-type scanning.Often row scanning result is the " leftmost side to recordScanning element coordinate " and " rightmost sideScanning element coordinate " (hereinafter referred to as).As scan line withoutUnit, then jump to next line.
If scan line only has 1Unit, then the coordinate for also recording the unit isCoordinate.
, will be all with reference to the definition of Cc coordinate-systems after completing all 64 row scanningsThe x values and y values point of coordinate data
8 are not multiplied by, then map that to Pql images(8x, 8y).
Since y value minimum points, connected one by one along clock-wise order allDOCgql can be formed.
Will be all above-mentionedCoordinate is stored into database with JSON forms.
(5) .Pql images DOCgql interior zones highlight method
After DOCgql extractions are completed, if necessary to exclude to disturb without streakline regional background, it may be used as hereinbefore
The rectangle lattice of size takes supplementary set with DOCgql inner regions, and shadow mask layer is set to supplementary set region using GDI+ methods, you can
The streakline for highlighting fingerprint on site leaves area image.As shown in Figure 3.
Step S03：Fingerprint on site streakline leaves reconstruction of the region on than middle stamp fingerprint.
This step uses mode identification technology, and the circumference data reconstruction that fingerprint on site streakline is left to region is right to its
The streakline corresponding region on Pkt images is answered, particular technique method is as follows：
The calculating of S03.1, the matching of optimal details " reference coordinate is with reference to data " Tm
This step is using " assigning power bigraph (bipartite graph) model " (hereinafter referred to as bigraph (bipartite graph)), and Kuhn-Munkres algorithms are (hereinafter referred to as
K-M algorithms) and " rigid body variation model " to S01 steps extract Mql and its ratio in relation pair answer analyzing for Mkt, preferably most
Good details matching local mode, " the reference coordinate reference of Pkt corresponding regions is corresponded to as DOCgql is mapped to it
Data " (hereinafter referred to as Tm).This step specific method is as follows：
(1) fingerprint on site-stamp fingerprint minutiae matching assigns the data of power bigraph (bipartite graph) model (hereinafter referred to as BGltm models)
Structure and parameter defines
Establish array minu pMnt [N] and represent all details, N is the number of details.In bigraph (bipartite graph)
Often with a one details of vertex representation.Define two-dimensional array pW [M] [N] and represent each details in bigraph (bipartite graph)
Match weight.
(2) assigns the modeling of power bigraph (bipartite graph)
Represent that the rotation translation between each pair " matching minutiae point " is unrelated using IFV (Invariant Feature Vector)
AmountThat is the matched local mode of minutia.As shown in Figure 4.
Centered on Pql details i, amount IFVi unrelated with the details construction rotation translation that distance i is nearest.
It is unrelated with the details construction rotation translation that distance j is nearest centered on the details j on Pkt with i Corresponding matchings
Measure IFVj.
If wijMatching similarity between IFVi and IFVj.
In above formula, qiThe picture quality Qrql, q of coordinate position where details ijSat where details j
The picture quality Qrql, q of cursor positionjAlgorithm it is identical with S02.1 (2) .c step the methods.
Using full details characteristic point on Pql and Pkt as vertex, wijFor weight, construction assigns power bigraph (bipartite graph), as shown in Figure 5.
(3) is directed to BGltm models, using Kuhn-Munkres algorithms, by preferably calculating, draws optimal Tm.
Tm is analyzed in S03.2, use " rigid body translation model ", and fingerprint on site streakline is calculated and leaves regional reconstruction
" optimal rigid motion relation " (hereinafter referred to as M)
Matched for the details in Tm to pij, under the conditions of rigid body translation, using Pql and Pkt coordinate origins as rotation
Turn center, the rotation angle T of i, jθMeet following relation：
In above formula, (x, y) and (x', y') represents matching to coordinate respectively, i.e., represents that fingerprint on site image and ten refers to respectively
Coordinate in fingerprint image, TxAnd TyRepresent translation distance, TθRepresent rotation angle.
Calculate rigid motion parameter be：
Tx=xj-(xi cos Tθ-yi sin Tθ)
Ty=yj-xisinTθ+yicos Tθ)
Tθ=θj-θi
xi,yiAnd xj,yjPoint to be matched is represented respectively to pi,pjTransverse and longitudinal coordinate, θi,θjRepresent pi,pjDetails
Angle, TθRepresent rotation angle.pi,pjThe coordinate in fingerprint on site image and ten finger fingerprint images is represented respectively.
Define Mij=(Tx,Ty,Tθ) be i to j kinematic parameter.
Calculate to obtain and be possible to set of matches { pijMotion parameter set { Mij, average, obtain optimal rigid body fortune
Dynamic relation M=(Tx,Ty,Tθ)。
S03.3 is mapped to the streakline corresponding region on its correspondence Pkt image using M as parameter, by DOCgql data, obtains
" circumference reconstruction data ".
Movement relation M=(the T tried to achieve according to previous stepx,Ty,Tθ), the circumference data of fingerprint on site are carried out firm
Body converts, and obtains fingerprint on site streakline and leaves reconstruction data (hereinafter referred to as DRR) of the region on corresponding ten fingers fingerprint image, number
It is constant according to storage format.As shown in Fig. 6 a, Fig. 6 b.
Step S04：Scene of a crime fingerprint ridge leaves the dynamic reconstruction of region frequency.
This step will establish special statistical model, and dynamic reconstruction, particular technique method are carried out to the region frequency of DRR data
It is：
S04.01, establish DRR templates
The structure of DRR templates for 1280 pixel pure white square-shaped images of 5*2 1280 pixel * (referred to hereinafter as D1, D2,
D3 ... D10), the 01-10 for corresponding to ten finger fingerprints respectively refers to position.Whole grids are arranged according to 160 row * 160 standard grid (under
Literary abbreviation Md) subregion (length and width of i.e. each grid are the pixel of 8 pixels × 8) is carried out, whole Md are encoded according to position, if
Counter (hereinafter referred Mcd) statistical number of whole Md is 0.Using each square area upper left corner as coordinate origin, establish flat
Face rectangular coordinate system, if the position coordinates of Md is its corresponding transverse and longitudinal lattice number (x, y).It is shown in Table one.As shown in Figure 7.
Table one
| Numbering | Refer to position | Numbering | Refer to position |
| D1 | Hand thumb | D6 | Left hand thumb |
| D2 | Right hand forefinger | D7 | Left index finger |
| D3 | Right hand middle finger | D8 | Left hand middle finger |
| D4 | Right hand fourth finger | D9 | Left hand fourth finger |
| D5 | Right hand little finger of toe | D10 | Left hand little finger of toe |
(" scene of a crime fingerprint ridge leaves region frequency dynamic reconstruction number for acquisition for the region frequency reconstruction of S04.02, DRR
According to ", hereinafter referred to as Fdrr)
(1), the extraction and conversion of DRR area datas
Using 1*1 units, the scanning sliding window that step value is 1 carries out Z-type scanning to DRR data, records fingerprint on site
Streakline leaves the Md coordinate point datas corresponding to reconstruction regions.After the completion of scanning, DRR regions are scaled one group of Md coordinate points
Grid data.
(2), the assignment of Mcd and the computational methods of " scene of a crime fingerprint ridge leaves region frequency dynamic reconstruction data "
Repeat step S01, S02, S03, whole until system of fingerprints memory storage compare relation datas (and its what is be related to show
Fingerprint and stamp archives fingerprint) DRR area informations have been processed into, and by whole DRR area datas be reconstituted in D1,
On D2, D3 ... D10, you can obtain " scene of a crime fingerprint ridge leaves region frequency dynamic reconstruction data " Fdrr.
The figure methods of exhibiting of S04.03, Fdrr data
" planar multi-color frequency thermal map mode " can be used (to be converted into the Fdrr regularities of distribution of Mcd for the displaying of Fdrr data
" visible ray optical wavelength thermal map " and show user), or use " 3 D stereo frequency figure " (Fdrr of Mcd to be distributed rule
Rule is converted into height curved surface height and shows user), it can form dynamic, " scene of a crime fingerprint ridge is lost for reflection in real time
Stay region frequency " graphical analysis result.
(1) " visible ray optical wavelength thermal map " methods of exhibiting of .Fdrr data
A) Mcd values maximum is set as max, is grouped Md by finger position, abscissa, ordinate, if Md frequency ratio=Mcd/
Max, the reference point shown as Mcd colors, is stored in database.
When b) showing, the Mcd distribution situations of whole Md are shown with visible light full spectrum.The Md that ratio is 0 is white, with
The rise of Mcd frequency, successively with orange, yellow, green, blue, indigo, 16 system color codes ' #ffffff', ' #ff0000' of purple, ' #
FF7F00', ' #FFFF00', ' #00ff00', ' #00ffff', ' #0000ff', ' #ff00ff' carries out color to Md and show.This
Sample, is achieved that streakline is left the full spectrum dynamic image of position channel zapping rule and shown.As shown in Figure 8.
(2) " the 3 D stereo frequency figure " of .Fdrr data
A) Mcd values maximum is set as max, is grouped Md by finger position, abscissa, ordinate, if Md frequency ratio=Mcd/
Max, the reference point highly shown as Mcd, is stored in database.
B) during 3D display, the maximum height that max is coloured curved surface is taken, shows that its corresponding has according to the corresponding ratio of Md
Color curved surface height, you can realize that streakline is left the 3 D stereo of position channel zapping rule and shown.As shown in Figure 9.
Claims (10)
1. a kind of scene of a crime fingerprint ridge leaves region frequency dynamic reconstruction method, it is characterised in that comprises the following steps：
Fingerprint on site streakline overlay area circumference line is identified and data are extracted；
Fingerprint on site streakline is left region on than middle stamp fingerprint to be rebuild；
Carry out the dynamic reconstruction that scene of a crime fingerprint ridge leaves region frequency.
2. a kind of scene of a crime fingerprint ridge according to claim 1 leaves region frequency dynamic reconstruction method, its feature
Be it is described fingerprint on site streakline overlay area circumference line is identified and data extraction comprise the following steps：
Step S02.1, extraction fingerprint on site leaves streakline and is converted into structuring streakline data Sgql；
Step S02.2, extraction and storage organization streakline dataControl point, Cgql curves；
Step S02.3, fingerprint on site streakline overlay area circumference line number evidence is extracted.
3. a kind of scene of a crime fingerprint ridge according to claim 2 leaves region frequency dynamic reconstruction method, its feature
It is that the step S02.1 comprises the following steps：
(1) picture noise is removed to fingerprint on site image Pql and obtains fingerprint on site enhancing figure Peql；
(2) prospect streakline is extracted, and region segmentation is carried out according to Quality estimation, excludes engineer's scale lines；
(3) extraction and calibration of the field of direction：First ask for removing the Peql field of directions of engineer's scale using gradient algorithm and do smooth place
Reason, further according to Qrql values, the direction by the direction field data in Qrql high level region in adjacent masses region to Qrql low values region
Field data is covered, and obtains fingerprint on site general direction field data Dql；
(4) calculating of ridge frequency：The direction field parameters provided using Dql, along Peql streakline direction projections；Measure One Dimensional Projection
The extreme point of figure, obtains the frequency of extreme point, is ridge frequency Frql；
(5) Gabor filtering algorithms are used, streakline sharpening processing is carried out to Peql using Dql and Frql as parameter, and will be switched off
Streakline reconnects, and obtains the Gabor filter result data Pgql of Peql；
(6) binaryzation：Binaryzation is carried out to Pgql and obtains bianry image Bgql；
(7) streakline refines：Bgql is changed into the refinement figure of setting pixel wide, i.e. ridge alignment by Hilditch algorithms
Tgql；
(8) tracking and data storage of streakline are refined：Streakline endpoint whole in Tgql is detected, then using whole endpoints as starting
Point, tracks each streak line of Tgql one by one, will form the coordinate of each pixel of streakline according to Ridge following sequential storage
Get up, that is, obtain the structuring streakline data Sgql of Tgql.
4. a kind of scene of a crime fingerprint ridge according to claim 3 leaves region frequency dynamic reconstruction method, its feature
It is described (2) extraction prospect streakline, and region segmentation is carried out according to Quality estimation, excluding engineer's scale lines includes following step
Suddenly：
A) orientation consistency algorithm process Peql each points are used, extraction has the regular i.e. orientation consistency parameter in direction positioned at setting
In the range of texture region, leave region Rrql as fingerprint ridge, fingerprint ridge left to other areas beyond the Rrql of region
Domain is as the background area Riql unrelated with fingerprint ridge；
B) threshold value of connectivity analysis algorithm and the threshold value of orientation consistency algorithm are set, according to above-mentioned two algorithms modified R rql and
Riql data, form continuous distribution；
C) image quality index of Rrql pixel resolutions, contrast and contrast is obtained, these index weighted sums obtain
The comprehensive quality index Qrql of each pixel comprehensive qualities of reflection prospect Prql；If Prql is divided into by dry mass according to Qrql numerical value
Region Pgql；
D) detect in Peql with the presence or absence of a plurality of straightway, that is, engineer's scale of parallel equidistant arrangement；It is as existed, then these are parallel
Region labeling where equidistant line section is engineer's scale region, and removes engineer's scale.
5. a kind of scene of a crime fingerprint ridge according to claim 2 leaves region frequency dynamic reconstruction method, its feature
It is that the step S02.2 comprises the following steps；
(1) definition structure streakline dataControl point structure, Cgql curvilinear structures；
(2) extract and storeControl point and Cgql curves；
Sgql data are called, since the streakline starting point of left side, untill the streakline terminating point of right side, streakline is extracted every distance d
Certain upper point is used as control pointDefining starting point and right side terminating point on the left of streakline at the same time is
First will all it extractBe stored in by control point structure in relevant database SDB1, then by Sgql data according to
Cgql curvilinear structures are stored in relevant database SDB1.
6. a kind of scene of a crime fingerprint ridge according to claim 2 leaves region frequency dynamic reconstruction method, its feature
It is that step S02.3 comprises the following steps：
(1) is establishedModel：Establish and be used to reflectPoint existence dataMatrix model；
(2) settings scanning sliding window；
(3) establishes fingerprint on site MDOCgql models, for storing DOCgql data distribution range datas；
(4) is extracted and storage fingerprint on site leaves area periphery contour line control point
Z-type scanning is carried out to MDOCgql wholes cell data using scanning sliding window；Often row scanning result is the leftmost side to recordScanning element coordinate and the rightmost sideScanning element coordinate, and be denoted asAs scan line withoutUnit, then jump to next line；If scan line only has 1Unit, then also record the seat of the unit
It is designated asCoordinate；
, will be all after completing all row scanningsThe x values and y values of coordinate data are multiplied by 8 and obtain a little respectivelyThen will
It is mapped to Pql images
Since y value minimum points, connected one by one along clock-wise order allForm DOCgql；
Will be all above-mentionedCoordinate is stored into database.
7. a kind of scene of a crime fingerprint ridge according to claim 1 leaves region frequency dynamic reconstruction method, its feature
In it is described fingerprint on site streakline left into region carry out reconstruction on than middle stamp fingerprint comprise the following steps：
Step S03.1, optimal details matching reference coordinate is asked for reference to data Tm；
Step S03.2, analysis is carried out to Tm the optimal rigid motion relation that fingerprint on site streakline leaves regional reconstruction is calculated
M；
Step S03.3 is mapped to the streakline corresponding region on its correspondence Pkt image using M as parameter, by DOCgql data, obtains outer
Enclose contour reconstruction data.
8. a kind of scene of a crime fingerprint ridge stated according to claim 7 leaves region frequency dynamic reconstruction method, its feature exists
Comprise the following steps in step S03.1：
(1) defines the data structure and parameter that fingerprint on site-stamp fingerprint minutiae matching assigns power bigraph (bipartite graph) Model B Gltm：
Establish array minu pMnt [N] and represent all details, N is the number of details；It is each in bigraph (bipartite graph)
One details of vertex representation；Define the matching power that two-dimensional array pW [M] [N] represents each details in bigraph (bipartite graph)
Weight；
(2) assigns the modeling of power bigraph (bipartite graph)
Represent that each pair between the characteristic Mql of fingerprint on site and the characteristic Mkt of stamp fingerprint matches details using IFV
Rotation between point translates unrelated amountThat is the matched local mode of minutia；
Centered on Pql details i, amount IFVi unrelated with the details construction rotation translation that distance i is nearest；With
Pkt is upper with centered on the details j of i Corresponding matchings, rotating the unrelated amount of translation with the details construction that distance j is nearest
IFVj；
Matching similarity between IFVi and IFVj
In above formula, qiThe picture quality Qrql, q of coordinate position where details ijThe coordinate bit where details j
The picture quality Qrql put；
Using full details characteristic point on Pql and Pkt as vertex, wijFor weight, construction assigns power bigraph (bipartite graph)；
(3) is directed to BGltm models, and data Tm is drawn using Kuhn-Munkres algorithms.
9. a kind of scene of a crime fingerprint ridge according to claim 7 leaves region frequency dynamic reconstruction method, its feature
It is that step S03.2 comprises the following steps：
Matched for the details in Tm to pij, under the conditions of rigid body translation, using Pql and Pkt coordinate origins as in rotation
The heart, the rotation angle T of i, jθMeet following relation：
<mrow>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<msup>
<mi>x</mi>
<mo>&prime;</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<mi>y</mi>
<mo>&prime;</mo>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>cosT</mi>
<mi>&theta;</mi>
</msub>
</mrow>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<msub>
<mi>sinT</mi>
<mi>&theta;</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>sinT</mi>
<mi>&theta;</mi>
</msub>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>cosT</mi>
<mi>&theta;</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mi>x</mi>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>y</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>+</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<msub>
<mi>T</mi>
<mi>x</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>T</mi>
<mi>y</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
(x, y) and (x', y') represents matching to coordinate, T respectivelyxAnd TyRepresent translation distance, TθRepresent rotation angle；
Calculate rigid motion parameter be：
Tx=xj-(xicosTθ-yisinTθ)
Ty=yj-(xisinTθ+yicosTθ)
Tθ=θj-θi
Define Mij=(Tx,Ty,Tθ) be i to j kinematic parameter；
Calculate to obtain and be possible to set of matches { pijMotion parameter set { Mij, average, obtain optimal rigid motion and close
It is M=(Tx,Ty,Tθ), pijRepresent possible match point.
10. a kind of scene of a crime fingerprint ridge according to claim 1 leaves region frequency dynamic reconstruction method, its feature
It is that the dynamic reconstruction that the progress scene of a crime fingerprint ridge leaves region frequency comprises the following steps：
S04.01, establish DRR templates：The structure of DRR templates is ten square chart pictures, corresponds to the ten respectively finger positions for referring to fingerprint respectively；
Whole grids are subjected to subregion according to grid Md, whole Md are encoded according to position；
S04.02, the region frequency reconstruction for carrying out DRR, obtain scene of a crime fingerprint ridge and leave region frequency dynamic reconstruction data
Fdrr；
(1), the extraction and conversion of DRR area datas
DRR data are scanned using scanning sliding window, record fingerprint on site streakline leaves the Md corresponding to reconstruction regions
Coordinate point data；After the completion of scanning, DRR regions are scaled to the grid data of one group of Md coordinate points；
(2), by whole DRR data reconstructions on each finger position, you can obtain scene of a crime fingerprint ridge and leave region frequency dynamic
Rebuild data Fdrr；
S04.03, by Fdrr data pass through graphical display.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711128381.1A CN107909031B (en) | 2017-11-15 | 2017-11-15 | Crime scene fingerprint line leaving area frequency dynamic reconstruction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711128381.1A CN107909031B (en) | 2017-11-15 | 2017-11-15 | Crime scene fingerprint line leaving area frequency dynamic reconstruction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107909031A true CN107909031A (en) | 2018-04-13 |
| CN107909031B CN107909031B (en) | 2021-06-08 |
Family
ID=61845517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711128381.1A Active CN107909031B (en) | 2017-11-15 | 2017-11-15 | Crime scene fingerprint line leaving area frequency dynamic reconstruction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107909031B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108416342A (en) * | 2018-05-28 | 2018-08-17 | 杭州电子科技大学 | A kind of fingerprint identification method of combination minutiae point and filament structure |
| CN109003259A (en) * | 2018-06-25 | 2018-12-14 | 张威 | Fingerprint quality evaluation method based on streakline Quality Expert's visual cognition machine learning |
| CN109271890A (en) * | 2018-08-29 | 2019-01-25 | 墨奇科技（北京）有限公司 | The method and apparatus that automatic fingerprint image extracts |
| CN109977879A (en) * | 2019-03-28 | 2019-07-05 | 山东省计算中心（国家超级计算济南中心） | A kind of acquisition of fingerprint on site matches control methods and system with long-range |
| CN110674745A (en) * | 2019-09-24 | 2020-01-10 | 山东省计算中心（国家超级计算济南中心） | Fingerprint restoration method and system based on field legacy fingerprints |
| WO2020042035A1 (en) * | 2018-08-29 | 2020-03-05 | Moqi Technology (beijing) Co., Ltd. | Method and device for automatic fingerprint image acquisition |
| CN113642102A (en) * | 2021-07-23 | 2021-11-12 | 一汽奔腾轿车有限公司 | Automatic modeling method for rigid body pair in collision model |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1595428A (en) * | 2004-07-15 | 2005-03-16 | 清华大学 | Fingerprint identification method based on density chart model |
| WO2005059809A2 (en) * | 2003-12-15 | 2005-06-30 | Infineon Technologies Ag | Method for identifying a fingerprint and fingerprint sensor |
| CN101770567A (en) * | 2008-12-31 | 2010-07-07 | 杭州中正生物认证技术有限公司 | Method for identifying biological features |
| CN103559476A (en) * | 2013-09-16 | 2014-02-05 | 中国联合网络通信集团有限公司 | Fingerprint matching method and device thereof |
| US20140294262A1 (en) * | 2013-04-02 | 2014-10-02 | Clarkson University | Fingerprint pore analysis for liveness detection |
| CN104361331A (en) * | 2014-12-05 | 2015-02-18 | 南京信息工程大学 | Fingerprint matching method based on bipartite graph optimal matching |
| CN104834923A (en) * | 2015-06-01 | 2015-08-12 | 西安电子科技大学 | Fingerprint image registering method based on global information |
| CN105138959A (en) * | 2015-07-28 | 2015-12-09 | 苏州南光电子科技有限公司 | Image processing based fingerprint matching and control method |
| CN105740753A (en) * | 2014-12-12 | 2016-07-06 | 比亚迪股份有限公司 | Fingerprint identification method and fingerprint identification system |
-
2017
- 2017-11-15 CN CN201711128381.1A patent/CN107909031B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005059809A2 (en) * | 2003-12-15 | 2005-06-30 | Infineon Technologies Ag | Method for identifying a fingerprint and fingerprint sensor |
| CN1595428A (en) * | 2004-07-15 | 2005-03-16 | 清华大学 | Fingerprint identification method based on density chart model |
| CN101770567A (en) * | 2008-12-31 | 2010-07-07 | 杭州中正生物认证技术有限公司 | Method for identifying biological features |
| US20140294262A1 (en) * | 2013-04-02 | 2014-10-02 | Clarkson University | Fingerprint pore analysis for liveness detection |
| CN103559476A (en) * | 2013-09-16 | 2014-02-05 | 中国联合网络通信集团有限公司 | Fingerprint matching method and device thereof |
| CN104361331A (en) * | 2014-12-05 | 2015-02-18 | 南京信息工程大学 | Fingerprint matching method based on bipartite graph optimal matching |
| CN105740753A (en) * | 2014-12-12 | 2016-07-06 | 比亚迪股份有限公司 | Fingerprint identification method and fingerprint identification system |
| CN104834923A (en) * | 2015-06-01 | 2015-08-12 | 西安电子科技大学 | Fingerprint image registering method based on global information |
| CN105138959A (en) * | 2015-07-28 | 2015-12-09 | 苏州南光电子科技有限公司 | Image processing based fingerprint matching and control method |
Non-Patent Citations (2)
| Title |
|---|
| WEI TANG 等: "Reconstructing Ridge Frequency Map from Minutiae Template of Fingerprints", 《2013 IEEE SIXTH INTERNATIONAL CONFERENCE ON BIOMETRICS: THEORY, APPLICATIONS AND SYSTEMS (BTAS)》 * |
| 刘晓莉: "嵌入式指纹识别系统的研究与设计", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108416342A (en) * | 2018-05-28 | 2018-08-17 | 杭州电子科技大学 | A kind of fingerprint identification method of combination minutiae point and filament structure |
| CN108416342B (en) * | 2018-05-28 | 2022-02-18 | 杭州电子科技大学 | Fingerprint identification method combining thin node and thin line structure |
| CN109003259A (en) * | 2018-06-25 | 2018-12-14 | 张威 | Fingerprint quality evaluation method based on streakline Quality Expert's visual cognition machine learning |
| CN109003259B (en) * | 2018-06-25 | 2022-02-18 | 张威 | Fingerprint quality evaluation method based on line quality expert visual cognition machine learning |
| CN109271890A (en) * | 2018-08-29 | 2019-01-25 | 墨奇科技（北京）有限公司 | The method and apparatus that automatic fingerprint image extracts |
| WO2020042035A1 (en) * | 2018-08-29 | 2020-03-05 | Moqi Technology (beijing) Co., Ltd. | Method and device for automatic fingerprint image acquisition |
| CN109271890B (en) * | 2018-08-29 | 2021-07-13 | 墨奇科技（北京）有限公司 | Method and apparatus for automatic fingerprint image extraction |
| CN109977879A (en) * | 2019-03-28 | 2019-07-05 | 山东省计算中心（国家超级计算济南中心） | A kind of acquisition of fingerprint on site matches control methods and system with long-range |
| CN110674745A (en) * | 2019-09-24 | 2020-01-10 | 山东省计算中心（国家超级计算济南中心） | Fingerprint restoration method and system based on field legacy fingerprints |
| CN113642102A (en) * | 2021-07-23 | 2021-11-12 | 一汽奔腾轿车有限公司 | Automatic modeling method for rigid body pair in collision model |
| CN113642102B (en) * | 2021-07-23 | 2024-03-15 | 一汽奔腾轿车有限公司 | Automatic modeling method for rigid body pairs in collision model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107909031B (en) | 2021-06-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107909031A (en) | A kind of scene of a crime fingerprint ridge leaves region frequency dynamic reconstruction method | |
| CN105956582B (en) | A kind of face identification system based on three-dimensional data | |
| Zhang | Image analysis | |
| CN106339998B (en) | Multi-focus image fusing method based on contrast pyramid transformation | |
| CN104951940B (en) | A kind of mobile payment verification method based on personal recognition | |
| Maltoni et al. | Synthetic fingerprint generation | |
| Feng et al. | Robust and efficient algorithms for separating latent overlapped fingerprints | |
| CN107169479A (en) | Intelligent mobile equipment sensitive data means of defence based on fingerprint authentication | |
| CN106778468A (en) | 3D face identification methods and equipment | |
| Bouchaffra et al. | Structural hidden Markov models for biometrics: Fusion of face and fingerprint | |
| Ambeth Kumar et al. | Exploration of an innovative geometric parameter based on performance enhancement for foot print recognition | |
| CN103679136A (en) | Hand back vein identity recognition method based on combination of local macroscopic features and microscopic features | |
| CN110232390A (en) | Image characteristic extracting method under a kind of variation illumination | |
| CN108875645A (en) | A kind of face identification method under the conditions of underground coal mine complex illumination | |
| CN107679494B (en) | Fingerprint image matching method based on selective extension | |
| CN113822157A (en) | Mask wearing face recognition method based on multi-branch network and image restoration | |
| CN109523484B (en) | Fractal feature-based finger vein network repair method | |
| CN106778491A (en) | The acquisition methods and equipment of face 3D characteristic informations | |
| CN109359607A (en) | A kind of palm print and palm vein fusion identification method based on texture | |
| CN105373781A (en) | Binary image processing method for identity authentication | |
| CN110866442B (en) | Real-time face recognition-based testimony-of-person integrated checking system and method | |
| Costa et al. | Towards biometric identification using 3D epidermal and dermal fingerprints | |
| CN106980845A (en) | The crucial independent positioning method of face based on structured modeling | |
| CN109285204B (en) | Biological key generation method for fusing fingerprint and finger vein bit levels | |
| CN109753912A (en) | A kind of multi-light spectrum palm print matching process based on tensor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |