Abstract
The dual-modal biometric recognition based on feature-level fusion is an important research direction in identity recognition. To improve the performance of identity recognition, we propose a novel dual-modal biometric recognition method based on weighted joint sparse representation classification (WJSRC). The method introduces joint sparse representation classification (JSRC) to fuse fingerprint and finger-vein features at first. Then, a penalty function is constructed between the test and training samples to optimize the sparse representation. Finally, the image quality scores of samples are utilized to construct a weight function to optimize the decision-making. The experimental results on two bimodal datasets demonstrate that the proposed method has significant improvement for the accuracy and reliability of identity recognition.
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Fang, C., Ma, H., Yang, Z. (2021). A Novel Dual-Modal Biometric Recognition Method Based on Weighted Joint Sparse Representation Classifaction. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_1
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DOI: https://doi.org/10.1007/978-3-030-86608-2_1
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