Abstract: In some implementations, a computer-implemented method includes: obtaining a captured image including, (i) a latent fingerprint to be captured, and (ii) a template, surrounding the latent fingerprint, that contains plurality of symbols; extracting, using an image processing technique, one or more characteristics for each of the plurality of symbols; calculating a resolution for the captured image based at least on the extracted one or more characteristics for each of the plurality of symbols; generating a reconstructed desired image, based at least on (i) the calculated resolution of the captured image, and (ii) the one or more extracted characteristics for each of the plurality of symbols; and providing the reconstructed desired image to a fingerprint identification system to perform a particular fingerprint operation.

Abstract: In some implementations, systems and techniques are capable of performing a multi-stage tattoo matching operation to improve matching accuracy for various tattoo images—namely small tattoo images, distorted tattoo images, low-quality tattoo images. The multi-stage tattoo matching operation described throughout includes two matching stages that compare a search tattoo image against a set of reference tattoo images. In the first matching stage, the system can use scale invariant feature transform (SIFT) descriptors, or other similar types of local descriptors, to identify a list of best-matched reference tattoo images from among the set of reference tattoo images. In the second matching stage, the system uses enhanced tattoo matching techniques to verify the non-matched points and determine whether their associated areas are matched or not.

Abstract: In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.

Abstract: In some implementations, a computer-implemented method for recognizing facial images may include a multi-stage facial verification process to improve the speed and accuracy of a facial recognition operation. For example, a facial recognition module may include multiple stages where a subset of data is analyzed recursively to improve the speed of the facial recognition processes. The multiple stages may be arranged in a fast-to-slow and coarse-to-fine arrangements such that a match decision may be made at each successive stage.

Abstract: In some implementations, systems and techniques are capable of performing a multi-stage tattoo matching operation to improve matching accuracy for various tattoo images—namely small tattoo images, distorted tattoo images, low-quality tattoo images. The multi-stage tattoo matching operation described throughout includes two matching stages that compare a search tattoo image against a set of reference tattoo images. In the first matching stage, the system can use scale invariant feature transform (SIFT) descriptors, or other similar types of local descriptors, to identify a list of best-matched reference tattoo images from among the set of reference tattoo images. In the second matching stage, the system uses enhanced tattoo matching techniques to verify the non-matched points and determine whether their associated areas are matched or not.

Abstract: In some implementations, a computer-implemented method includes: obtaining a captured image including, (i) a latent fingerprint to be captured, and (ii) a template, surrounding the latent fingerprint, that contains plurality of symbols; extracting, using an image processing technique, one or more characteristics for each of the plurality of symbols; calculating a resolution for the captured image based at least on the extracted one or more characteristics for each of the plurality of symbols; generating a reconstructed desired image, based at least on (i) the calculated resolution of the captured image, and (ii) the one or more extracted characteristics for each of the plurality of symbols; and providing the reconstructed desired image to a fingerprint identification system to perform a particular fingerprint operation.

Abstract: A fingerprint matching system for fingerprint matching using virtual minutiae includes a fingerprint scanning device, including (i) an interface to a fingerprint scanner, and (ii) a sensor associated with the fingerprint scanner, configured to identify a match between a search fingerprint representing a fingerprint of a subject and a reference fingerprint from among a plurality of reference fingerprints representing fingerprints of a plurality of historical subjects; a database containing the plurality of reference fingerprints representing fingerprints of the plurality of historical subjects; and a computing device in communication with said database and said fingerprint scanning device.

Abstract: In some implementations, a method may be used for matching palm print images. A search template that identifies at least a plurality of distinctive search orientation blocks within a search orientation field may be initially generated for a search palm print image. A reference template may be obtained. A mated distinctive reference orientation block may be identified for each of the distinctive search orientation blocks. One or more regions of the search orientation field that include the distinctive search orientation blocks may be compared against one or more corresponding regions of the reference orientation field. An orientation similarity score between the search palm print image and the reference palm print image may be computed based on the comparison. A match may finally be determined if the computed orientation score satisfies a predetermined threshold value.

Abstract: Systems and methods may be used by an automatic fingerprint identification system to estimate patterns of a latent fingerprint. A latent fingerprint image, and a plurality of reference ridge flow maps may initially be obtained. Each reference ridge flow map may be associated with a particular fingerprint pattern. A latent ridge flow map for the obtained latent fingerprint image may be computed. One or more characteristics associated with the latent ridge flow map may be compared to one or more characteristics associated with each of the plurality of reference ridge flow maps. A similarity score between the latent ridge flow map and a particular reference ridge flow map may be computed for each of the plurality of reference ridge flow maps. One or more fingerprint patterns present within the latent fingerprint may then be determined and provided for output.

Abstract: In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.

Abstract: A fingerprint matching system for fingerprint matching using virtual minutiae includes a fingerprint scanning device, including (i) an interface to a fingerprint scanner, and (ii) a sensor associated with the fingerprint scanner, configured to identify a match between a search fingerprint representing a fingerprint of a subject and a reference fingerprint from among a plurality of reference fingerprints representing fingerprints of a plurality of historical subjects; a database containing the plurality of reference fingerprints representing fingerprints of the plurality of historical subjects; and a computing device in communication with said database and said fingerprint scanning device.

Abstract: In some implementations, a computer-implemented method includes an iterative minutiae matching technique that initially groups mated minutiae within a list of all possible minutiae between a reference fingerprint and a search fingerprint, and then successively performs a geometric consistency check of the mated minutiae within the group. In some instances, the iterative matching technique enables improved identification of globally aligned mated minutiae within distorted regions, which may be used to subsequently improve the calculation of a similarity score between a reference fingerprint and a search fingerprint.

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

Abstract: In some implementations, a computer-implemented method for recognizing facial images may include a multi-stage facial verification process to improve the speed and accuracy of a facial recognition operation. For example, a facial recognition module may include multiple stages where a subset of data is analyzed recursively to improve the speed of the facial recognition processes. The multiple stages may be arranged in a fast-to-slow and coarse-to-fine arrangements such that a match decision may be made at each successive stage.

Abstract: In one aspect, methods that use a novel representation, referred to as an octant feature vector (OFV), are used for matching distorted fingerprints. For instance, a feature-based matcher for distorted fingerprint matching may use a two-step local and global matching scheme to compare a set of feature vectors that are derived from minutiae of a reference fingerprint and a search fingerprint. The relative geometric relationships between the reference minutia and nearest minutiae may be derived and encoded into a feature vector based on orientation difference. The OFV is invariant to the rigid transformations and is insensitive to nonlinear distortions since the relative geometric relationships are independent from the rigid transformation.

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

Abstract: In some implementations, a computer-implemented method for recognizing facial images may include a multi-stage facial verification process to improve the speed and accuracy of a facial recognition operation. For example, a facial recognition module may include multiple stages where a subset of data is analyzed recursively to improve the speed of the facial recognition processes. The multiple stages may be arranged in a fast-to-slow and coarse-to-fine arrangements such that a match decision may be made at each successive stage.

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

Abstract: In one aspect, methods that use a novel representation, referred to as an octant feature vector (OFV), are used for matching distorted fingerprints. For instance, a feature-based matcher for distorted fingerprint matching may use a two-step local and global matching scheme to compare a set of feature vectors that are derived from minutiae of a reference fingerprint and a search fingerprint. The relative geometric relationships between the reference minutia and nearest minutiae may be derived and encoded into a feature vector based on orientation difference. The OFV is invariant to the rigid transformations and is insensitive to nonlinear distortions since the relative geometric relationships are independent from the rigid transformation.

Abstract: In some implementations, a computer-implemented method includes an iterative minutiae matching technique that initially groups mated minutiae within a list of all possible minutiae between a reference fingerprint and a search fingerprint, and then successively performs a geometric consistency check of the mated minutiae within the group. In some instances, the iterative matching technique enables improved identification of globally aligned mated minutiae within distorted regions, which may be used to subsequently improve the calculation of a similarity score between a reference fingerprint and a search fingerprint.