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Robust face recognition using generalized neural reflectance model

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Abstract

A generalized neural reflectance (GNR) model for enhancing face recognition under variations in illumination and posture is presented in this paper. Our work is based on training a number of synthesis images of each face taken at single lighting direction with frontal/posture view. This way of synthesizing images can be used to build training cases for each face under different known illumination conditions from which face recognition can be significantly improved. However, reconstructing face shape may not easily be achieved and the human face images usually form by highly complex structure which suffers from strong specular and unknown reflective conditions. In this paper, these limitations are addressed by Cho and Chow (IEEE Trans Neural Netw 12(5):1204–1214, 2002). Face surfaces are recovered by this GNR model and face images in different poses are synthesized to create a database for training. Our training algorithm assigns to recognize the face identity by similarity measure on face features extracting first by the principle component analysis (PCA) method and then further processing by the Fisher’s discrimination analysis (FDA) to acquire lower dimensional patterns. Experimental results conducted on the Yale Face Database B show that lower error rates of classification and recognition are achieved under different variations in lighting and pose and the performance significantly outperforms the recognition without using the proposed GNR model.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. The work was supported by School of Computer Engineering, Nanyang Technological University, Singapore under a start-up research grant (CE-SUG 10/03).

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Authors and Affiliations

  1. Division of Computing Systems, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, Singapore, 639798

    Siu-Yeung Cho

  2. Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong, China

    Tommy W. S. Chow

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  1. Siu-Yeung Cho
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  2. Tommy W. S. Chow
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Correspondence to Siu-Yeung Cho.

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Cho, SY., Chow, T.W.S. Robust face recognition using generalized neural reflectance model. Neural Comput & Applic 15, 170–182 (2006). https://doi.org/10.1007/s00521-005-0017-7

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  • DOI: https://doi.org/10.1007/s00521-005-0017-7

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