Deep learning-driven palmprint and finger knuckle pattern-based multimodal Person recognition system

Abstract

Biometric recognition systems are widely being used in several applications due to its distinctiveness and reliability. In recent years, hand-based person recognition has received much momentum due to its stability, feature richness, reliability and higher user acceptability. In this paper, we propose a multimodal hand biometric system based on Finger Knuckle Print (FKP) and Palmprint. In particular, the PCANet deep learning method is employed to extract distinctive features from each modality. Then, multiclass SVM is utilized to compute a matching score for each individual modality. Finally, score level fusion is performed to combine the matching scores via different rules such as, min, sum, max and multiplication. The performance of the proposed system is evaluated on the publicly available database known as PolyU. First, we conducted several experiments on single FKP and Palmprint traits. Next, score level fusion based multimodal experiments were performed. The proposed framework was able to achieve 0.00% of EER (equal error rates) and 100% of rank-1 performance. In addition, the proposed system based on PCANet with score level fusion of FKP and Palmprint outperformed existing multimodal methods.