Transfer learning for bimodal biometrics recognition

Zhiping Dan; Shuifa Sun; Yanfei Chen; Haitao Gan

doi:10.1117/12.2031482

25 October 2013 Transfer learning for bimodal biometrics recognition

Zhiping Dan, Shuifa Sun, Yanfei Chen, Haitao Gan

Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 891918 (2013) https://doi.org/10.1117/12.2031482
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

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.

Citation Download Citation

Zhiping Dan, Shuifa Sun, Yanfei Chen, and Haitao Gan "Transfer learning for bimodal biometrics recognition", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891918 (25 October 2013); https://doi.org/10.1117/12.2031482

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