Multi-Modal Biometrics based on Data Fusion

• Published in: Journal of physics. Conference series Vol. 1684; no. 1; pp. 12023 - 12028
• Main Authors: Yang, Hongxun, Sun, Eason, Cheng, Cheng, Ding, Anthony H
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.11.2020
• Subjects: Access control; Algorithms; Biometric recognition systems; Biometrics; Data integration; Kernels; Machine learning; Modal data; Physics
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1684/1/012023

With the development of intelligent application, biometrics recognition technology has been widely concerned and applied in many fields of the real world, such as access control and payment. The traditional biometrics are usually based on single modality data of the subjects, but they are limited by the feature information capacity and the bottleneck in recognition accuracy. In this paper, a multi-modal biometric recognition framework is presented, which utilizes a multi-kernel learning algorithm to fuse heterogeneous information of different modal data. In order to extract complementary information from them, we combine the kernel matrix to form the mixed kernel matrix, and then give the final classification results. The experimental results on multiple biometric datasets show that our method can obtain higher recognition accuracy compared with the existing single mode and multi-mode fusion methods.