A Novel Hybrid Fusion Combining Palmprint and Palm Vein for Large-Scale Palm-Based Recognition
Dandan Fan, Xu Liang, Wei Jia, Junan Chen, David Zhang
Abstract
Palmprint and palm vein are emerging as unique biometric traits for identity authentication, each with its own advantages and limitations. Using these two traits jointly promises to enhance the discriminative and anti-spoofing capabilities. While existing research often combines these traits in parallel, such approaches lead to unnecessary increase in response time. Moreover, large-scale palm-based recognition, as a great potential task, raises higher requirements in accuracy and time efficiency. Nevertheless, few efforts have been dedicated to either data establishment or method investigation for this task. To this end, a large-scale palm-based multimodal dataset covering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ 20\,000 $</tex-math> </inline-formula> palms is proposed, far larger than any of its kind. We also propose a hybrid fusion method to leverage these two diverse features. Our method employs a two-stage recognition process. First, a dual likelihood ratio test for coarse recognition is designed to assign palms into imposter certainty, genuine certainty or uncertainty classes. The coarse recognition narrows down the number of possible identities accurately using one trait, palm vein, consuming less recognition time. Then, in fine recognition, an adaptive weighted fusion of palmprint and palm vein is proposed to delicately rerecognize the uncertainty subsets that are in doubt in the coarse recognition, resulting in a more discriminative capacities. Experimental results confirm the effectiveness of our method, showing improved recognition performance with high-time efficiency.