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Proceedings Paper

Transfer learning for bimodal biometrics recognition
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Paper Abstract

Biometrics recognition aims to identify and predict new personal identities based on their existing knowledge. As the use of multiple biometric traits of the individual may enables more information to be used for recognition, it has been proved that multi-biometrics can produce higher accuracy than single biometrics. However, a common problem with traditional machine learning is that the training and test data should be in the same feature space, and have the same underlying distribution. If the distributions and features are different between training and future data, the model performance often drops. In this paper, we propose a transfer learning method for face recognition on bimodal biometrics. The training and test samples of bimodal biometric images are composed of the visible light face images and the infrared face images. Our algorithm transfers the knowledge across feature spaces, relaxing the assumption of same feature space as well as same underlying distribution by automatically learning a mapping between two different but somewhat similar face images. According to the experiments in the face images, the results show that the accuracy of face recognition has been greatly improved by the proposed method compared with the other previous methods. It demonstrates the effectiveness and robustness of our method.

Paper Details

Date Published: 25 October 2013
PDF: 6 pages
Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891918 (25 October 2013); doi: 10.1117/12.2031482
Show Author Affiliations
Zhiping Dan, China Three Gorges Univ. (China)
Huazhong Univ. of Science and Technology (China)
Shuifa Sun, China Three Gorges Univ. (China)
Yanfei Chen, Huazhong Univ. of Science and Technology (China)
Haitao Gan, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 8919:
MIPPR 2013: Pattern Recognition and Computer Vision
Zhiguo Cao, Editor(s)

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