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: In some implementations, a method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

Abstract: In some implementations, an image recognition system is capable of using an iterative multi-stage image matching procedure to improve the accuracy and/or reliability of matching a search image with a small number of distinctive feature points to reference images. In the first stage, the system compares a search template for the search image to reference templates of each image within the set of reference images. The system determines a best-matched reference image from among the reference images of the set of reference images. In the second stage, the system compares the hybrid search template to one or more reference templates of reference images that is not the best-matched reference image. The comparison can be used to either compute match scores for reference images corresponding to the one or more reference templates.

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, 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, an image recognition system is capable of using an iterative multi-stage image matching procedure to improve the accuracy and/or reliability of matching a search image with a small number of distinctive feature points to reference images. In the first stage, the system compares a search template for the search image to reference templates of each image within the set of reference images. The system determines a best-matched reference image from among the reference images of the set of reference images. In the second stage, the system compares the hybrid search template to one or more reference templates of reference images that is not the best-matched reference image. The comparison can be used to either compute match scores for reference images corresponding to the one or more reference templates.

Abstract: Some implementations of the systems and techniques described include a tattoo recognition system that is capable of improving the accuracy and efficiency associated with matching edge curves extracted from tattoo images, which are typically performed during a tattoo matching operation. The system can perform a matching operation in two stages—a feature extraction stage and a matching stage. In the first stage, the system extracts one or more edge curves from the tattoo image. In the second stage, the system performs matching using a two-step comparison that compares features of the extracted edge curves.

Abstract: In some implementations, a method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

Abstract: A list of possible mated minutiae in a reference fingerprint and a search fingerprint is identified. The list of possible mated minutiae is divided into different groups of mated minutiae using pose grouping based on rigid transformation parameters. For each group, the possible mated minutiae are filtered to get a set of consistent mated minutiae pairs having similar rigid transformation. The topological consistency between groups is determined. If any two groups are topologically consistent to each other, they are merged to form a (virtual) larger group. A non-linear transformation parameters are estimated from the the mated minutiae in the merged group. The overall similarity of mated and non-mated minutiae in the overlapping area is determined after non-linear alignment of the two fingerprints, and the maximum similarity is output as the final similarity score between two fingerprints.

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: Systems and methods may be used by an automatic fingerprint identification system to estimate ridge flow maps in latent fingerprints. A latent fingerprint image and a plurality of reference ridge flow maps may initially be obtained. 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 reference ridge flow maps. The top closely matched ridge flow maps are used to improve the latent ridge flow map to enhance the latent fingerprint image to extract better set of minutia points to improve latent to ten-print matching accuracy.

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, a system is capable of improving the results of a matching operation by normalizing match scores with respect to quality levels of compared fingerprints and/or attributes of the compared fingerprints. The system initially obtains match data of the matching operation. The match data can include baseline record match scores for a set of reference records that are compared against a search record. The system then uses normalization techniques to normalize the values of the baseline record match scores with respect to, for example, quality levels of the search and reference records, and various attributes of the compared fingerprints, such as fingerprint quality, finger position, and detected fingerprint patterns. The normalized record match scores can therefore be used to more accurately represent the similarity between the search record and the reference records.

Abstract: In some implementations, an image recognition system is capable of using an iterative multi-stage image matching procedure to improve the accuracy and/or reliability of matching a search image with a small number of distinctive feature points to reference images. In the first stage, the system compares a search template for the search image to reference templates of each image within the set of reference images. The system determines a best-matched reference image from among the reference images of the set of reference images. In the second stage, the system compares the hybrid search template to one or more reference templates of reference images that is not the best-matched reference image. The comparison can be used to either compute match scores for reference images corresponding to the one or more reference templates.

Abstract: Systems and methods may be used to detect scale invariant features in an image. Circle like region points may be detected from a plurality of edge maps produced from a plurality of smoothed images. Edge maps are obtained by taking partial derivatives of multi-scaled smoothed images and the smoothed images might be obtained from down scaled image and convolved with different scaled filters. The strong ridges are kept for circle detection based on the scale-normalized edge strength and direction of the point. A 2-1 Hough Transform is used to detect the circles at the radius as a function of the standard deviation of the filter. Only the strong circle points are kept to calculate the descriptors. Multi descriptors are calculated at the regions around the circle points from a plurality of images. The images might include the smoothed images, the difference of the images and the edge map images.

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 method includes: receiving, from the camera, a sample image that includes a fingerprint and a mensuration reference device, where the sample image is associated with a resolution; identifying (i) a plurality of edge candidate groups within the sample image, and (ii) a set of regularity characteristics associated with each of the plurality of edge candidate groups; determining that the associated set of regularity characteristics indicates the mensuration reference device; identifying a ruler candidate group, from each of the plurality of edge candidate groups, based at least on determining that the associated set of regularity characteristics indicates the mensuration reference device; computing a scale associated with the sample image based at least on extracting a set of ruler marks from the identified ruler candidate group; and generating, based at least on the scale associated with the sample image, a scaled image.

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.