Abstract
Biometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. In this paper, a bimodal hand identification system was proposed based on Scale Invariant Feature Transform (SIFT) descriptors, extracted from hand shape and palmprint modalities. A local sparse representation method was adopted in order to represent images with high discrimination. Moreover, fusion was performed at feature and decision levels using a cascade fusion in order to generate the final identification rate of our bimodal system. Our experiments were applied on two hand databases: Indian Institute of Technology of Delhi (IITD) hand database and Bosphorus hand database containing, respectively, 230 and 615 subjects. The results show that the proposed method offers high accuracies compared to other popular bimodal hand biometric methods over the two hand databases. The correct identification rate reaches 99.57 % which is competitive compared to systems existing in the literature.
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