Robust orientation field estimation and extrapolation using semilocal line sensors
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Multimodal biometric identification system based on finger geometry, knuckle print and palm print
Pattern Recognition Letters
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Partial palmprint matching using invariant local minutiae descriptors
Transactions on data hiding and multimedia security V
Feature level fusion of face and palmprint biometrics
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Stockwell transform based palm-print recognition
Applied Soft Computing
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Spatiotemporal analysis of human activities for biometric authentication
Computer Vision and Image Understanding
Phase congruency induced local features for finger-knuckle-print recognition
Pattern Recognition
Analysis of performance of palmprint matching with enforced sparsity
Digital Signal Processing
Dual phase learning for large scale video gait recognition
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Palmprint based recognition system using phase-difference information
Future Generation Computer Systems
Palmprint matching using feature points and SVD factorisation
Digital Signal Processing
Journal of Real-Time Image Processing
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The evidential value of palmprints in forensics is clear as about 30% of the latents recovered from crime scenes are from palms. While palmprint-based personal authentication systems have been developed, they mostly deal with low resolution (about 100 ppi) palmprints and only perform full-to-full matching. We propose a latent-to-full palmprint matching system that is needed in forensics. Our system deals with palmprints captured at 500 ppi and uses minutiae as features. Latent palmprint matching is a challenging problem because latents lifted at crime scenes are of poor quality, cover small area of palms and have complex background. Other difficulties include the presence of many creases and a large number of minutiae in palmprints. A robust algorithm to estimate ridge direction and frequency in palmprints is developed. This facilitates minutiae extraction even in poor quality palmprints. A fixed-length minutia descriptor, MinutiaCode, is utilized to capture distinctive information around each minutia and an alignment-based matching algorithm is used to match palmprints. Two sets of partial palmprints (150 live-scan partial palmprints and 100 latents) are matched to a background database of 10,200 full palmprints to test the proposed system. Rank-1 recognition rates of 78.7% and 69%, respectively, were achieved for live-scan palmprints and latents.