A feature extraction method for use with bimodal biometrics |
| |
Authors: | Yong Xu [Author Vitae] Jing-Yu Yang [Author Vitae] |
| |
Affiliation: | a Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China b Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong c School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China |
| |
Abstract: | Bimodal biometrics has been found to outperform single biometrics and are usually implemented using the matching score level or decision level fusion, though this fusion will enable less information of bimodal biometric traits to be exploited for personal authentication than fusion at the feature level. This paper proposes matrix-based complex PCA (MCPCA), a feature level fusion method for bimodal biometrics that uses a complex matrix to denote two biometric traits from one subject. The method respectively takes the two images from two biometric traits of a subject as the real part and imaginary part of a complex matrix. MCPCA applies a novel and mathematically tractable algorithm for extracting features directly from complex matrices. We also show that MCPCA has a sound theoretical foundation and the previous matrix-based PCA technique, two-dimensional PCA (2DPCA), is only one special form of the proposed method. On the other hand, the features extracted by the developed method may have a large number of data items (each real number in the obtained features is called one data item). In order to obtain features with a small number of data items, we have devised a two-step feature extraction scheme. Our experiments show that the proposed two-step feature extraction scheme can achieve a higher classification accuracy than the 2DPCA and PCA techniques. |
| |
Keywords: | Principal component analysis Feature extraction Matrix-based complex PCA (MCPCA) Palmprint identification Biometrics |
本文献已被 ScienceDirect 等数据库收录! |
|