Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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
  • G06V40/1306—Sensors therefor non-optical, e.g. ultrasonic or capacitive sensing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T5/00—Image enhancement or restoration
  • G06T5/70—Denoising; Smoothing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/20—Image preprocessing
  • G06V10/30—Noise filtering
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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
  • G06V40/1365—Matching; Classification
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10004—Still image; Photographic image
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30196—Human being; Person
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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
  • G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow

Definitions

• the present invention generally relates to a method for removing disturbances in an image captured by a fingerprint sensor, and specifically to reduction of noise in an acquired fingerprint image by incorporating the sensing principle into the applied method for noise reduction, wherein the noise reduced image is used for determining a representation of a fingerprint pattern.
• the invention also relates to a corresponding electronic device and to a computer program product.
• biometric systems are used more and more in order to provide for increased security for accessing an electronic device, thereby providing an enhanced user convenience.
• fingerprint sensors have been successfully integrated in such devices, for example, thanks to their small form factor, high performance and user acceptance.
• capacitive sensing is most commonly used, in particular in applications where size and power consumption are important issues.
• All capacitive fingerprint sensors provide a measure indicative of the capacitance between several sensing elements and a finger placed on the surface of the fingerprint sensor.
• Acquisition of a fingerprint image is typically performed using a fingerprint sensor comprising a plurality of sensing elements arranged in a two-dimensional manner, and a block based technique may be applied to the fingerprint sensor for acquiring a fingerprint image, where the blocks of sensing elements are sampled sequentially.
• a block of eight sensing elements adjacently arranged in one row may be sampled at the same time.
• the presence of noise in the sensor introduces an error into the data values that are read when sampling each block of sensing elements.
• This error manifests as a potentially varying offset from a certain zero-offset reference, such as ground. Because the blocks of sensing elements are scanned sequentially and because the amount of noise in the sensor may vary over time, a different error may occur in each block of sensing elements.
• This noise problem has traditionally been compensated by configuring the hardware of the fingerprint sensor.
• a software or firmware approach may be advantageous as the amount of compensation can be flexibly controlled.
• the software or firmware approach does not consume additional silicon area or silicon development schedule, and is relatively computationally inexpensive.
• US 2014/0015774 Al An exemplary software implementation for noise reduction is disclosed in US 2014/0015774 Al, where the acquired sensor data is adjusted in order to compensate for noise introduced by the fingerprint sensor.
• a redundant sensing element is introduced in regards to the block based sampling of the fingerprint sensor, where the same redundant sensing element will be sampled by each one of two sequentially sampled blocks.
• An offset is calculated based on a difference between the redundant sampling of the same sampling element, and the second block is adjusted based on the calculated difference.
• an object of the present invention to provide an improved method for handling of noise in a fingerprint image captured using a fingerprint sensor.
• the present inventors have found that the selection of an optimized sampling pattern is desirable, where knowledge as to the selected sampling pattern is included in further processing of the acquired fingerprint image.
• a method of determining a representation of a fingerprint pattern of a finger captured using a fingerprint sensor comprising a plurality of sensing elements comprising the steps of selecting a sampling matrix, wherein the sampling matrix represents a sampling pattern for acquiring a fingerprint image using the fingerprint sensor, acquiring the fingerprint image using the fingerprint sensor and according to the sampling matrix, applying a linear filter to the acquired fingerprint image, wherein setup of the linear filter depends on the sampling matrix and the linear filter is provided for noise reduction within the acquired fingerprint image, and determining the representation of said fingerprint pattern based on said filtered fingerprint image.
• the present invention is based upon the realization that a typical implementation of a system comprising a fingerprint sensor only allows a selected portion of the total sensor to be sampled at a single time.
• the typical acquisition of a fingerprint image using a fingerprint sensor comprises consecutively acquiring portions of the fingerprint image and combining these portions into one fingerprint image.
• time variant or random noise possibly being present at the time of acquiring each of the portions of the fingerprint image may be somewhat different for each of the portions of the fingerprint image.
• artifacts may be introduced in the image.
• a computer implemented method may be applied for post processing of the fingerprint image, where the sampling strategy used for acquiring the fingerprint image is taken into account.
• a linear filter will be applied to the fingerprint image, where the linear filter is setup in such a way that it takes into account the sampling strategy.
• the sampling strategy is in accordance to the invention realized as a sampling matrix which representing a sampling pattern for acquiring a fingerprint image using the fingerprint sensor.
• advantages with the invention include an improved formation of a fingerprint image and thus the possibility of better performance in regards to the
• the filtering scheme according to the invention closely relies on how in fact the fingerprint image has been acquired.
• the method is a computer implemented post processing scheme, there is no necessity of adjusting the hardware of the fingerprint sensor, a common prior art approach for noise mitigation. Rather, the inventive method may be inserted as a component in a typical flow for acquiring a fingerprint image. The inventive method will typically be able to handle any type of noise, being specifically useful in relation to common mode noise (CMN) as will be further elaborated below.
• CPN common mode noise
• inventive method typically is implemented as code executed by a processor controlling the fingerprint sensor or arranged separately with a system in which the fingerprint sensor forms an element
• inventive concept may alternatively (or partly) be implemented as functional blocks of for example an ASIC or similar. Any combination of such implementations are possible and within the scope of the invention.
• the sampling matrix will have a structure being based on the spatial implementation of the fingerprint sensor. The exact structure of the sampling matrix and the optimized selection of the sampling matrix will be further discussed below.
• the fingerprint sensor is a two-dimensional fingerprint sensor and the sampling matrix corresponds to a selected portion of the plurality of sensing elements.
• the fingerprint sensor may, as indicated above, be implemented using any kind of currently or future fingerprint sensing principles, including for example capacitive, optical, or thermal sensing technology. However, at present capacitive sensing is most preferred.
• one-dimensional sensors are possible and within the scope of the invention.
• the filtering scheme proposed by the inventive method will in addition to the sampling pattern used in acquiring a single fingerprint image also cater for subsequent sampling of a plurality of fingerprint images.
• the sampling matrix may be changed for subsequently captured fingerprint image.
• the sampling matrix may for example define a selected portion of the plurality of sensing elements as an adjacently arranged group of sensing elements.
• the selected portion of the plurality of sensing elements may be spatially separated over the two- dimensional sensor.
• the sampling matrix is selected to correspond to a minimized amount of noise within an acquired fingerprint image.
• the selection of the sampling matrix may be made in an iterative manner, where an amount of perceived noise within the acquired fingerprint image is reduced to a minimum.
• the selection may alternatively be computed based on predetermined assumptions in relation to the structure of the fingerprint sensor. This will be further elaborated in relation to the detailed description of the invention.
• the method further comprises the steps of determining a log- likelihood ratio for noise being present within the fingerprint image, comparing the log- likelihood ratio with a predetermined threshold, and performing filtering of the fingerprint image only if the log-likelihood ratio is above the predetermined threshold.
• the filtering scheme as discussed above will not be performed.
• the log-likelihood ratio in regards to a two-dimensional fingerprint sensor comprising a plurality of rows may be computed e.g. row-by-row and then filtered (e.g. averaged) over several rows (e.g. using a sliding window averaging filter).
• the step of determining a log-likelihood ratio comprises evaluating a likelihood function for the assumption that noise is not present, and evaluating the likelihood function for the assumption that noise is present in accordance with the sampling matrix.
• This implementation may have additional advantages as the sampling matrix is also taken into account in the case of assuming that noise is not present. However, such an implementation is provided as an alternative to the case where the sampling matrix is only taken into account in regards to the assumption that noise is present.
• the method further comprises the step of applying a non-linear mapping function to the acquired fingerprint image, where the non-linear mapping function for example may be a logarithmic function.
• Applying a non- linear mapping function to the acquired fingerprint image have the advantage that it make the multiplicative effects additive.
• a portable electronic device comprising a portable electronic device, comprising a fingerprint sensor having an array of pixels, and a control unit electrically connected to the fingerprint sensor, wherein the control unit is configured for selecting a sampling matrix, wherein the sampling matrix represents a sampling pattern for acquiring a fingerprint image using the fingerprint sensor, applying a linear filter to the acquired fingerprint image, wherein setup of the linear filter depends on the sampling matrix and the linear filter is provided for noise reduction within the acquired fingerprint image, and determining a representation of a fingerprint pattern based on said filtered fingerprint image.
• the invention provides similar advantages as discussed above in relation to the previous aspect of the invention.
• the invention provides for an improved reliability of the electronic device, for example in use cases where noise, such as common-mode noise (CMN) may readily be introduced.
• noise such as common-mode noise (CMN) may readily be introduced.
• CNN common-mode noise
• such a scenario may for example be when the electronic device is connected to a switch mode power supply.
• control unit is preferably an ASIC, a micro processor or any other type of computing device for controlling the operation of the fingerprint sensor.
• the control unit may form an integral part of the second user input device.
• control unit may also be a general control unit comprised with the portable electronic device, for example configured for controlling the overall operation of the electronic device.
• the fingerprint sensor may, as indicated above, be implemented using any kind of currently or future fingerprint sensing principles, including for example capacitive, optical, or thermal sensing technology. However, at present capacitive sensing is most preferred.
• the fingerprint sensor comprises at least 160x160 pixels, more preferably 192x192 pixels, and most preferably 208x80 pixels. Further resolutions are possible and within the scope of the invention.
• the portable electronic device may for example be a mobile phone or a tablet.
• the filtering may be carried out on analog or digital signals, and may be performed on the fingerprint sensor component or outside the fingerprint sensor component, such as in a host processor in a mobile phone or computer etc.
• a computer program product comprising a computer readable medium having stored thereon computer program means for a control unit adapted for controlling a portable electronic device, the portable electronic device comprising a fingerprint sensor and a control unit, wherein the computer program product comprises code for selecting a sampling matrix, wherein the sampling matrix represents a sampling pattern for acquiring a fingerprint image using the fingerprint sensor, code for acquiring the fingerprint image using the fingerprint sensor and according to the sampling matrix, code for applying a linear filter to the acquired fingerprint image, wherein setup of the linear filter depends on the sampling matrix and the linear filter is provided for noise reduction within the acquired fingerprint image, and code for determining a representation of a fingerprint pattern based on said filtered fingerprint image.
• this aspect of the invention provides similar advantages as discussed above in relation to the previous aspects of the invention.
• control unit is preferably an ASIC, a micro processor or any other type of computing device.
• a software executed by the control unit for operating the inventive system may be stored on a computer readable medium, being any type of memory device, including one of a removable nonvolatile random access memory, a hard disk drive, a floppy disk, a CD-ROM, a DVD-ROM, a USB memory, an SD memory card, or a similar computer readable medium known in the art.
• the present invention generally relates to a method for removing disturbances in an image captured by a fingerprint sensor, and specifically to reduction of noise in an acquired fingerprint image by incorporating the sensing principle into the applied method for noise reduction, wherein the noise reduced image is used for determining a representation of a fingerprint pattern.
• Advantages with the invention include enhanced determination of fingerprint patterns from fingerprint images captured using a fingerprint sensor.
• Fig 1 schematically illustrates an application for a fingerprint sensing system according to an example embodiment of the present invention
• Fig 2 is a representative illustration of common-mode noise from a switched power supply
• Fig 3 a schematically shows a first embodiment of the fingerprint sensing system according to the present invention
• Fig 3b schematically shows a second embodiment of the fingerprint sensing system according to the present invention
• Fig. 4 illustrates a noisy fingerprint image
• Fig. 5a - 5e illustrates different sampling configurations
• Fig. 6 is a flowchart disclosing the exemplary steps of the invention according to a currently preferred embodiment of the invention.
• Figs. 7a - 7e provides a functional illustration of the flowchart shown in Fig. 6.
• a fingerprint sensing system in the form of a mobile phone 1 with an integrated fingerprint sensing system 2.
• the fingerprint sensing system 2 may, for example, be used for unlocking the mobile phone 1 and/or for authorizing transactions carried out using the mobile phone etc.
• the mobile phone 1 is being charged using a charger 3 connected to an AC power socket 4.
• the charging of the phone may introduce comraon- mode noise that makes the mobile phone 1 follow the electric potential of the common-mode noise - the mobile phone 1 will "bounce up and down" electrically.
• the common-mode noise will, from the perspective of the mobile phone 1, cause the user to appear electrically noisy. This may influence uses of the mobile phone 1 that rely upon an evaluation of the potential difference between the mobile phone 1 and the user. Such uses include, for example, use of a capacitive touchscreen and use of the fingerprint sensing system 2.
• the mobile phone 1 shown in Fig. 1 may further comprises a first antenna for WLAN/Wi-Fi communication, a second antenna for telecommunication communication, a microphone, a speaker, and a phone control unit.
• a first antenna for WLAN/Wi-Fi communication may further comprise a first antenna for WLAN/Wi-Fi communication
• a second antenna for telecommunication communication may further comprised with the mobile phone.
• Further hardware elements are of course possibly comprised with the mobile phone.
• the invention may be applicable in relation to any other type of portable electronic device, such as a laptop, a remote control, a tablet computer, or any other type of present or future similarly configured device.
• Fig 2 is a representative illustration of the common-mode noise from the charger 3 in Fig 1.
• the common-mode noise may have a low frequency component (50/60 Hz depending on the AC power frequency) with relatively high amplitude, and a high frequency switch mode component (the enlarged part of Fig 2) with a lower amplitude.
• the high frequency component of the common-mode noise can cause problems for a fingerprint sensing system.
• Fig 3a schematically shows a touch sensor based fingerprint sensing system, in the form of packaged touch sensor component 9 preferably comprising a two-dimensional sensor array 10 and for example a conductive bezel or frame 11 for providing an excitation signal to the finger of the user.
• the sensor component 9 also comprises a power supply interface and a communication interface.
• the sensor array 10 comprises a large number of sensing elements, 12 (only one of the sensing elements has been indicated with a reference numeral to avoid cluttering the drawing), each being controllable to sense a distance between a sensing structure (top plate) comprised in the sensing element 12 and the surface of a finger contacting the top surface of the sensor array 10.
• a sensing structure top plate
• a first group 13 of sensing elements are marked 'S' for sensing, where the first group of sensing elements are all sensed together at one time.
• Fig 3b schematically shows a swipe sensor based fingerprint sensing system, in the form of packaged swipe sensor component 19 comprising a sensor array 20 and conductive strips 21a and 21b for providing an excitation signal to the finger of the user.
• the sensor component 19 also comprises a power supply interface and a communication interface.
• the sensor array 20 comprises one or several lines of sensing elements, 12 (only one of the sensing elements has been indicated with a reference numeral to avoid cluttering the drawing), each being controllable to sense a distance between a sensing structure (top plate) comprised in the sensing element 12 and the surface of a finger contacting the top surface of the sensor array 20.
• a group 23 of sensing elements are marked 'S' for sensing, in a similar manner as in regards to Fig. 3a indicating a group based sensing strategy.
• the fingerprint sensor components 9, 19 in Figs 3a - b may advantageously be manufactured using CMOS technology, but other techniques and processes may also be feasible. For instance, an insulating substrate may be used and/or thin-film technology may be utilized for some or all process steps of the manufacturing process.
• CMN complementary metal-oxide-semiconductor
• the CMN will therefore perfectly cancel out and hence not degrade the measurement of the desired signal.
• it can however be very challenging to achieve good rejection of CMN due to e.g. imperfect cancellation and parasitic capacitances to other signals not impacted by the same CMN.
• capacitances to earth ground which does not follow the CMN signal and therefore directly exposes the absolute CMN level.
• CMN capacitive measurement principle
• the signal level of CMN coming from e.g. certain switch-mode power supplies in chargers may be very large, e.g. 40V peak-to-peak, as compared to the drive voltage supplied to the bezel in e.g. a fingerprint sensing system.
• the large signal level of the CMN can therefore be a dominating interference in the resulting fingerprint image.
• the noise can typically be modeled as being multiplicative, i.e. the amount of CMN scales with the value of the fingerprint. It should be emphasized that the method according to the present invention also support other type's noise couplings, e.g. additive noise, and that a non-linear mapping function may be applied to the input signal, e.g. a logarithm function to make the
• a major advantage of the method according to the present invention is that it guides the configuration of the sensing principle utilized in order to improve the CMN mitigation and then afterwards is capable of incorporating this knowledge into the CMN detection, estimation and rejection.
• a three step process is provided, including 1) setting up the sampling to improve mitigation of CMN, 2) detecting if CMN is present, and if so, 3) provides a means for rejecting it by filtering.
• the filtering typically consists of either a 1- or 2-dimensional Linear Minimum Mean Square Error (LMMSE) estimate of the CMN followed, either explicitly or implicitly, by cancellation of the CMN estimate from the input.
• LMMSE Linear Minimum Mean Square Error
• the CMN estimate has been derived such that it takes the characteristic of the sampling process and the noise into account while it at the same time exploits the structure of signal of interest. This has the advantage that the impact of the sampling process can be assessed and optimized in order to provide the best rejection of the noise.
• the signal of interest would be the image of the fingerprint.
• GLRT Generalized Likelihood Ratio Testing
• CMN may be assumed to be constant over the spatial sampling dimension, i.e. between pixels in the image
• the sampling procedure is an important factor in order to separate the desired (fingerprint) signal from that of the CMN.
• the sampling procedure both spatially and temporally is therefore key to achieving good mitigation of the noise. Examples of such spatio-temporal sampling configurations, which may be combined as desired, are, with further reference to Figs. 5a - 5e:
• the identity matrix is denoted I and 0 or 1 are vectors (or matrices) where all the elements have the value 0 or 1.
• the matrix trace is indicated by tr ⁇ - ⁇ and E denotes the real valued domain.
• Y G E xW denote the image captured by sensing system where M is the number of rows and N is the number of columns in the image.
• the "vectorized" version of the matrix Y is given as
• S G E ⁇ B ⁇ is a spatial sampling matrix describing how the CMN vector, c G W ⁇ B ⁇ , impacts the image Y
• B is the spatial sampling block-size.
• the spatial sampling block- size hence specifies how many desired (fingerprint) samples are acquired at the same time and thus sharing the same realization of the CMN.
• the vector f G E W denotes the desired (fingerprint) signal as well as any additional noise present. It is well known that the LMMSE estimate of the linear model in equation 2 can be computed as
• This special case showing perfect estimation, and hence subsequent cancellation, of the CMN is achievable by having a specific sampling design which is optimized to exploit the assumed covariance structure of the fingerprint and may be highly challenging to implement in practice.
• the general principle is to optimize the sampling procedure in order to provide the best CMN estimate for subsequent cancellation by exploiting prior covariance knowledge of the fingerprint and the CMN.
• the general LMMSE filter described here can be used for estimating the CMN, but the complexity of this implementation will typically be large, and one possible approximation to this full solution can be obtained by utilizing a Kronecker structure over e.g. the horizontal and/or vertical dimensions. If the desired signal and the CMN can be assumed to only correlate locally, a (block) sliding-window approximation to the LMMSE filter may also be used either alone or together with the Kronecker structure to ease implementation.
• Let the CMN vector be given by c ⁇ JV(0, P C I), where P c denotes the power of the CMN driving signal and K denotes the normal distribution.
• the sensor system can be designed such that the image Y is constructed by sampling several pixels simultaneously and this can be incorporated in the structure of S.
• the covariance matrix ⁇ c represents the prior information about the common-mode noise.
• the Kronecker structured expressions above so far ⁇ c has been considered to be a scaled identity matrix as this simplifies the expression and leads to a lower complexity in implementation.
• a- priori knowledge concerning the CMN may be available and this can then be incorporated into ⁇ c for improved estimation accuracy.
• this Kronecker structure will only be possible to directly exploit if the log-likelihood term S T ⁇ 1 S and prior ⁇ c share a common basis. This may be hard to achieve and can make it difficult to efficiently incorporate prior information about the CMN.
• a possible solution to this problem is to approximate the prior using the basis from the log-likelihood term or vice versa.
• the bases used could then be extracted from the log- likelihood term S T ⁇ 1 S or a common basis could be enforced for both the log-likelihood and prior terms, e.g. the fixed DCT basis.
• GLRT Generalized Likelihood Ratio Test
• Equation 11 Equation 11 where CMN being present will be declared if the log-likelihood ratio score LLR exceeds a given threshold.
• An alternative approach for computing an LLR for CMN detection is to directly evaluate the log-likelihood ratio using normalized covariance matrices as
• the matrices needed to compute the chosen LLR may be either precomputed offline and put into a filter bank or may be computed online in response to updated estimates of ⁇ f +c and ⁇ f . If desired, a low-rank approximation of the matrix F in the alternative approach may be utilized to ease implementation.
• one option is to perform a ID LMMSE estimate and GLRT of the CMN by processing each row (or column) independently.
• the advantage of such a process is that this will lower the computational complexity of the LMMSE estimation.
• GLRT Generalized Likelihood Ratio Testing
• a log-likelihood value i.e. perform GLRT
• the GLRT outputs one log-likelihood value per row (or column) and these values can be filtered / averaged over several rows (or columns) before they are compared against a threshold value which is used for CMN detection.
• the filtering of log- likelihood values may also be done by performing a sliding window averaging and the output of this can be compared against a threshold value.
• FIG. 6 is a flow chart showing the basic steps according to the invention for determining a fingerprint pattern using fingerprint sensor.
• Figs. 7a - 7e provides a corresponding functional illustration of some of the method steps.
• the fingerprint sensor provided in relation to Fig. 7a is a two-dimensional sensor exemplified as having 8x8 pixels. It should be noted that the use of 8x8 pixels is only used for providing a simplified explanation of the inventive method. Thus, any number of pixels may be used. As discussed above in relation to Fig.
• such a fingerprint sensor comprises a plurality of sensing element, each sensing element having an output for providing a pixel signal indicative of the capacitive coupling between the corresponding sensing element and a finger.
• other types of fingerprint sensors may however be used, including for example one-dimensional sensors and sensors employing different sensing techniques.
• fingerprint ( ⁇ ) and noise (CMN) ( ⁇ c ) covariance matrices are selected. Based on the selected covariance matrices, a sampling procedure is determined and specified by a sampling matrix (S) as shown in Fig.
• the size of the sampling matrix (S) is defined by the size of the fingerprint sensor and the block size for sampling of a selected few of the sensing elements. In the illustration provided, the sampling size is selected to be four (i.e. sampling of four pixels at a time). According to the discussion provided in relation to Equation 2, the sampling matrix (S) is defined as
• a sampling matrix (S) is defined as comprising 64 rows and 16 columns.
• each of the columns of the sampling matrix shown in Fig. 7b will represent each sensor sampling.
• the sampling pattern is selected to correspond to the sampling strategy shown in Fig. 5b, i.e. a squared block based pattern of 2x2 pixels, sliding from the top left of the sensor to the bottom right of the sensor.
• the ones shown in Fig. 7b indicate the pixels to be sampled for each sequential sample; zeroes are shown for pixels not being sampled.
• other sampling strategies may be selected, preferably selected in such a way that the amount of noise within the acquired fingerprint image is minimized.
• the fingerprint sensor is sampled accordingly and a fingerprint image is acquired.
• the fingerprint image is a noisy image, where the noise pattern is dependent on the block based sequential sampling strategy discussed above.
• the noise detection scheme as discussed above in relation to equation 11 and based on the selected sampling matrix is applied for determining the likelihood of noise being present in the acquired fingerprint image.
• the likelihood is determined by setting up two competing models, the first model being based on the assumption that noise is present, and the other model being based on the assumption that noise is not present.
• at least the model used where noise is assumed to be present takes into account the selected sampling matrix. It may also be possible to take into account the selected sampling matrix in relation to the model used where noise is assumed not to be present.
• a log-likelihood ratio is determined and compared to a predetermined threshold. In case it is determined that the likelihood of noise (in comparison to the threshold) is "too high", the process continues to a step of estimation of the noise, such as CMN, using a LMMSE filter as is discussed above in relation to equations 5 - 10.
• the acquired, noisy, fingerprint image is subsequently filtered using the "sampling matrix dependent filter", typically being a Wiener filter, and a "clean" image comprising less noise is formed, as is shown in Fig. 7d.
• the "block artifacts” have been removed.
• control functionality of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system.
• Embodiments within the scope of the present disclosure include program products comprising machine- readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor.
• machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor.
• any such connection is properly termed a machine-readable medium.
• Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

Landscapes

• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• Multimedia (AREA)
• Human Computer Interaction (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Life Sciences & Earth Sciences (AREA)
• Health & Medical Sciences (AREA)
• Biophysics (AREA)
• Cardiology (AREA)
• General Health & Medical Sciences (AREA)
• Heart & Thoracic Surgery (AREA)
• Physiology (AREA)
• Image Input (AREA)
• Collating Specific Patterns (AREA)
• Image Processing (AREA)
• Measurement Of Length, Angles, Or The Like Using Electric Or Magnetic Means (AREA)

Priority Applications (4)

Applications Claiming Priority (2)

Publications (1)

Family

ID=55067802

Family Applications (1)

Country Status (7)

Cited By (2)

Families Citing this family (25)

Citations (4)

Family Cites Families (36)

• 2015
  • 2015-04-29 US US14/698,930 patent/US9519819B2/en active Active
  • 2015-05-29 TW TW104117384A patent/TWI596551B/zh active
  • 2015-06-30 EP EP15822436.0A patent/EP3170124A4/en not_active Withdrawn
  • 2015-06-30 WO PCT/SE2015/050760 patent/WO2016010470A1/en not_active Ceased
  • 2015-06-30 JP JP2017501274A patent/JP6681382B2/ja active Active
  • 2015-06-30 KR KR1020167034196A patent/KR101875051B1/ko active Active
  • 2015-06-30 CN CN201580002541.0A patent/CN105706111B/zh active Active
• 2016
  • 2016-11-10 US US15/347,892 patent/US9779281B2/en active Active

Patent Citations (4)

Also Published As

Similar Documents

Legal Events