A Novel Representation of Palm-Print for Recognition

Abstract

AbstractThis paper proposes a novel palm-print feature extraction technique which is based on binarising the difference of Discrete Cosine Transform coefficients of overlapping circular strips. The binary features of palm-print are matched using Hamming distance. The system is evaluated using PolyU database consisting of 7,752 images. A procedure to extract palm-print for PolyU dataset is proposed and found to extract larger area compared to preprocessing technique in [1]. Variation in brightness of the extracted palm-print is corrected and the contrast of its texture is enhanced. Compared to the systems in [1, 2], the proposed system achieves higher Correct Recognition Rate (CRR) of 100 % with lower Equal Error Rate (EER) of 0.0073% at low computational cost.KeywordsDiscrete Cosine TransformLocal Binary PatternZernike MomentEqual Error RateIris RecognitionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.