This work proposes Block-wise uniform local binary pattern histogram (BULBPH) followed by kernel discrimination analysis (KDA) as descriptor for palmprint recognition. BULBPH provides distribution of uniform patterns (such as line and wrinkles) in local region and can be better used as palmprint features. KDA is applied on BULBPH to reduce dimension and enhance discriminative capability using chi-RBF kernel. The experiments are conducted on four palmprint databases and performance is compared with related descriptors. It is observed that KDA on BULBPH descriptor achieves more than 99% accuracy with 4.04 decidability index on four palmprint databases.
CITATION STYLE
Tamrakar, D., & Khanna, P. (2020). Discriminative BULBPH Descriptor with KDA for Palmprint Recognition. In Advances in Intelligent Systems and Computing (Vol. 1022 AISC, pp. 423–435). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-32-9088-4_35
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