Abstract
As one of the major branches in Face Recognition (FR), 2D-3D Heterogeneous FR (HFR), where face comparison is made across the texture and shape modalities, has become more important due to its scientific challenges and application potentials. In this paper, we propose a novel and effective approach, which adapts the Deep Canonical Correlation Analysis (Deep CCA) network to such an issue. Two solutions are presented to speed up the training process and improve the recognition accuracy so that Deep CCA better fits the problem of matching different types of face images. Thanks to the deep structure, the proposed approach hierarchically learns the mapping between 2D and 3D face clues and shows distinct superiority to the previous hand-crafted feature based techniques. Experiments are carried out on the FRGC v2.0 database, and the results achieved clearly demonstrate its competency.
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Acknowledgement
This work was partly supported by the Hong Kong, Macao, and Taiwan Science and Technology Cooperation Program of China (Grant No. L2015TGA9004 and 008/2014/AMJ) and the National Natural Science Foundation of China (No. 61673033).
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Wang, S., Huang, D., Wang, Y., Tang, Y. (2017). 2D-3D Heterogeneous Face Recognition Based on Deep Canonical Correlation Analysis. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_9
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DOI: https://doi.org/10.1007/978-3-319-69923-3_9
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