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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References
Andrew, G., Arora, R., Bilmes, J., Livescu, K.: Deep canonical correlation analysis. In: ICML, pp. 1247–1255 (2013)
Wang, W., Arora, R., Livescu, K., Bilmes, J.A.: Unsupervised learning of acoustic features via deep canonical correlation analysis. In: IEEE ICASSP, pp. 4590–4594 (2015)
Riccio, D., Dugelay, J.-L.: Asymmetric 3D/2D processing: a novel approach for face recognition. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 986–993. Springer, Heidelberg (2005). doi:10.1007/11553595_121
Rama, A., Tarres, F., Onofrio, D., Tubaro, S.: Mixed 2D–3D information for pose estimation and face recognition. In: IEEE ICASSP, p. II (2006)
Yang, W., Yi, D., Lei, Z., Sang, J., Li, S.Z.: 2D–3D face matching using CCA. In: IEEE FG, pp. 1–6 (2008)
Huang, D., Ardabilian, M., Wang, Y., Chen, L.: Asymmetric 3D/2D face recognition based on LBP facial representation and canonical correlation analysis. In: IEEE ICIP, pp. 3325–3328 (2009)
Huang, D., Ardabilian, M., Wang, Y., Chen, L.: Automatic asymmetric 3D–2D face recognition. In: IEEE/IAPR ICPR (2010)
Huang, D., Soltana, W.B., Ardabilian, M., Wang, Y., Chen, L.: Textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion. In: IEEE CVPR Workshop (2011)
Toderici, G., Passalis, G., Zafeiriou, S., Tzimiropoulos, G.: Bidirectional relighting for 3D-Aided 2D face recognition. In: IEEE CVPR, pp. 2721–2728 (2010)
Zhang, W., Huang, D., Wang, Y., Chen, L.: 3D aided face recognition across pose variations. In: Zheng, W.-S., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds.) CCBR 2012. LNCS, vol. 7701, pp. 58–66. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35136-5_8
Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate Analysis (Probability and Mathematical Statistics). Academic Press, San Diego (1980)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Lin, M., Chen, Q., Yan, S.: Network in network. In: ICLR (2014)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: IEEE CVPR, pp. 947–954 (2005)
Huang, G.B., Ramesh, M., Berg, T., Erik, L.M.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report (2007)
Huang, D., Ardabilian, M., Wang, Y., Chen, L.: Oriented gradient maps based automatic asymmetric 3D–2D face recognition. In: IEEE ICB, pp. 125–131 (2012)
Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE T-SMCC 41(6), 765–781 (2011)
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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