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  Fingerprint representation using localized texture featuresUSPTO Application #: 20070297653Title: Fingerprint representation using localized texture features Abstract: A system and method for processing fingerprints includes representing each minutiae in a fingerprint by determining quantized Gabor coefficients to represent texture content of the minutiae. A distance is computed between represented minutiae and stored minutiae. The minutiae matches are ranked based on the distance to identify the fingerprint. (end of abstract)
Agent: Keusey, Tutunjian & Bitetto, P.C. - Woodbury, NY, US Inventors: Rudolf Maarten Bolle, Sharat Suresh Chikkerur, Sharathchandra UmapathiRao Pankanti, Nalini Kanta Ratha USPTO Applicaton #: 20070297653 - Class: 382124 (USPTO) The Patent Description & Claims data below is from USPTO Patent Application 20070297653. Brief Patent Description - Full Patent Description - Patent Application Claims
BACKGROUND [0001]1. Technical Field [0002]The present invention relates to fingerprint processing and more particularly to a system and method for employing Gabor coefficients in a texture representation for fingerprint recognition. [0003]2. Description of the Related Art [0004]Biometric identifiers, such as, fingerprints, face, iris and voice prints, offer a way of reliable personal authentication. Biometrics is rapidly replacing traditional token and password methods. Of all the biometric modalities, fingerprints have emerged as a popular choice due to their universality, distinctiveness, permanence and acceptability. Another reason for their popularity is the wide variety of implementations of recognition algorithms that are already available. [0005]Existing fingerprint matching algorithms may be broadly classified into the following categories based on fingerprint representation. [0006]1. Correlation based: In this representation, the fingerprint image itself is used as a template. Matching is performed by measuring the result of cross correlation between the two images. This requires reasonably low resolution images and is very fast, since correlation may also be implemented through optical techniques. However, the matching is global and requires an accurate registration of the target and reference fingerprint images, since correlation is not invariant to translation and rotation. The accuracy of correlation based techniques further degrades with non-linear distortion of the fingerprint. [0007]2. Minutiae Representation: Minutiae represent local fingerprint ridge discontinuities and mark the position where a ridge comes to an end or bifurcates into two. Given target and reference fingerprints and their corresponding minutiae features, the process of matching is a point pattern matching problem. This is by far the most popular approach to fingerprint recognition. However, minutiae based matching algorithms do not perform well in the case of small fingerprints that have very few minutiae. Furthermore, minutiae based systems completely ignore gray scale content of the fingerprint images. Human experts routinely utilize the rich structural and texture cues present in the fingerprint image during the process of matching. Minutiae based representations do not encode this information either explicitly or implicitly. [0008]3. Texture Descriptors: A fingerprint image can also be viewed as a pattern of oriented texture formed by the gray scale variation of the ridges. Therefore, texture descriptors provide a good representation for the ridge content in the image. A global texture descriptor scheme called `finger code` utilizes both global and local ridge descriptions. The features are extracted by measuring the responses of radial fingerprint image sectors to a filterbank. The matching is based on measuring the Euclidean distance between the feature vectors. A disadvantage of this approach is that it requires that the fingerprint core is accurately located. This is a difficult problem in itself. [0009]Thus, algorithms based on minutiae require a point matching algorithm, but do not measure gray scale content. On the other hand, correlation and texture based methods measure gray scale content but require very accurate alignment. Another issue with the above mentioned techniques is that of scalability. It has been shown that algorithms designed for 1:1 verification scale poorly when used for 1:N identification tasks. [0010]Fingerprint recognition based on localized information selects `interesting` regions in the fingerprint image to perform local gray scale correlation. Plain ridges do not carry any information except their orientation, ridge frequency, ridge endings and ridge bifurcations. The `interesting` regions (similar to distinctive minutiae configurations) include regions around the minutiae, regions of high curvature and regions around the singular points such as core and delta. However, the optimal process of selecting these `interesting` regions is very inefficient. Furthermore, the algorithm is not robust to rotations. Localized correlation is used in other methods, but only in conjunction with a geometry based minutiae matcher. The localized correlation is used to assess the quality of the pairs obtained by the minutiae matcher. The localized correlation itself is not accurate enough to be useful. Furthermore, the algorithms are designed for 1:1 verification and are therefore not directly scalable for large scale identification tasks. SUMMARY [0011]A system and method for processing fingerprints includes representing each minutiae in a fingerprint by determining quantized Gabor coefficients to represent texture content of the minutiae. A distance is computed between represented minutiae and stored minutiae. The minutiae matches are ranked based on the distance to identify the fingerprint. [0012]Another method for processing fingerprints includes inputting a fingerprint image, identifying minutiae in the fingerprint image, computing Gabor coefficients to represent texture content of each minutiae in the fingerprint, and quantizing the Gabor coefficients for storage in memory. [0013]A system for processing fingerprints includes an input device configured to receive fingerprint images, an extraction module configured to determine minutiae in the fingerprint, and a representation module configured to compute Gabor coefficients to represent texture content for each minutiae to represent the fingerprint. A comparing module computes a distance between represented minutiae in the fingerprint and stored minutiae in memory to rank matches based on the distance to associate an identify with the fingerprint. [0014]These and other objects, features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS [0015]The disclosure will provide details in the following description of preferred embodiments with reference to the following figures wherein: [0016]FIG. 1A is an image showing relative geometry used for minutiae based matchers; [0017]FIG. 1B is an image showing the gray scale image content used in correlation and texture based matchers; [0018]FIG. 2A shows gray scale images of illustrative flow pattern configurations; [0019]FIG. 2B shows gray scale images of illustrative highly distinctive flow pattern configurations; [0020]FIG. 3 shows image representations of Gabor basis functions used to represent minutiae neighborhoods; [0021]FIG. 4 shows gray scale images of minutiae neighborhoods reconstructed in accordance with present principles; Continue reading... Full patent description for Fingerprint representation using localized texture features Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Fingerprint representation using localized texture features patent application. Patent Applications in related categories: 20080247614 - Biometric information obtaining apparatus - The apparatus enables a user to recognize the way he is moving his finger with respect to, for example, a sweep-type fingerprint sensor so that the user can easily and surely learn an appropriate way the finger (body part) should be moved. A velocity detecting means detects a velocity at ... 20080247613 - Fingerprint identification apparatus and portable electronic device having same - A fingerprint identification apparatus includes at least three light sources, a light guide, a camera module and a processor. The light guide has a top and a bottom surfaces and at least three side surfaces. 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