Filterbank-based fingerprint matching

By Anil K. Jain, Salil Prabhakar, Lin Hong, Sharath Pankanti. IEEE Transactions on Image Processing, 2000

1,249 citations recorded by the source corpus.

With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.

Subfields: Computer Vision and Pattern Recognition, Safety Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2978a9ec41ac0b900cd84dcf9b7bd2a8fabe2f3b

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Citations in the corpus

Palmprint identification using feature-level fusion (2006, result): “The results demonstrate that the proposed method is comparable with the previous palmprint approaches and other hand-based biometric technologies, including hand geometry and fingerprint verification [3,13].”
Efficient Privacy-Preserving Biometric Identification (2011, methodology): “Note that we are not evaluating the fingerprint matching algorithm here, since our privacy-preserving protocol uses exactly the original filterbank-based fingerprint matching algorithm which has been extensively evaluated [7].”
Palmprint feature extraction using 2-D Gabor filters (2003, background): “6 been widely used in various applications [10-19].”
Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement (2015, methodology): “A biometric system can be operated in two modes: (1) verification mode and (2) identification mode (Jain et al. 2000).”
Personal Identification Based on Iris Texture Analysis (2003, methodology): “In the spatial domain, one can extract information of an image at a certain orientation and scale using some specific filters, such as Gabor filters [33], [34], [35], [36], [37].”
Practical Biometric Authentication with Template Protection (2005, methodology): “This feature vector is inspired by FingerCode [ Jai00 ], but it can be calculated more efficiently since a rectangular grid is used rather than a circular one, and it performs better.”
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints (2002, background): “Finally, in [ 25 ], the ratio of the sines of the DFs in two adjacent regions is used as a measure to detect SPs.”
Fingerprint Matching Using an Orientation-Based Minutia Descriptor (2003, methodology): “These include methods based on optical correlation [4], [5], transform features [6], [7], [8], graph matching [9], structural matching [10], [11], and point pattern matching [3], [12], [13].”
On the individuality fingerprints (2001, neutral): “See, for example, [41].”
Decision-level fusion in fingerprint verification (2001, methodology): “Texture-based representation of $ngerprint image overcomes some of the problems with minutiae-based representation and captures both the local and the global information in a $ngerprint as a compact FingerCode [ 25 ].”
Fingerprint recognition by combining global structure and local cues (2006, methodology): “(12) Jain et al. [6] assumed that and are independent stochastic variables, thus the joint density function can be computed as the product of the two one-dimensional (1-”
Gabor Filter-Based Fingerprint Anti-spoofing (2008, background): “It is well known that, Gabor filters can optimally capture local frequency and orientation information [ 2 ], [3]; so we use Gabor features to characterize textural differences in real and spoof fingerprints.”
Online Palmprint Identification (2003, result): “This result is comparable with previous palmprint approaches and other hand-based biometric technologies including hand geometry and fingerprint verification [5], [6], [7], [11], [13], [14], [18], [19].”