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Robust two-stage face recognition approach using global and local features

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Abstract

This paper presents a robust two-stage face recognition approach that combines the traits of global features in first stage and the local features in second stage. The global features are extracted from Zernike moments (ZMs) method that encompasses the useful characteristics of being invariant to image rotation, scale, and noise. The local features are obtained from the histogram-based Weber Law Descriptor (WLD) having tremendous qualities like invariance to scale, change in image intensities, rotation, and noise. The novelty of this paper is twofold: (1) an efficient approach is used for combining the global and local features which is based on the human psychology to trace and memorize the known persons, i.e., locate some similar faces from the overall appearance of different persons and later identify from this the specific individual on the basis of their interior differences like shape of eyes, nose, etc.; (2) a method is used for providing the weights to individual face patches in extraction of local features, which is based on the averaged discrimination competence of features within a patch. The performance of proposed two-stage face recognition approach is analyzed against some major hurdles of this system, i.e., illumination, expression, scale, pose, occlusion, and noise variations. The proposed method achieves the highest recognition rate of 98.0% and 94.1% on ORL and Yale databases, respectively. The experimental results on these well-known face databases demonstrate that the proposed method is highly robust to illumination variation and also generates superior results against other variations.

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Acknowledgements

The authors are grateful to All India Council for Technical Education (AICTE), Govt. of India, New Delhi, India, for supporting the research work vide their file number 8013/RID/BOR/RPS-77/2005-06. We are also thankful to anonymous reviewers whose comments helped us to improve the quality of the paper.

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Authors and Affiliations

  1. Department of Computer Science, Punjabi University, Patiala, India

    Chandan Singh

  2. Department of Computer Science, South Asian University, New Delhi, India

    Ekta Walia

  3. Department of CSE&IT, Rayat & Bahra College of Engg. & Bio-Tech, Kharar, Distt. Mohali, India

    Neerja Mittal

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  1. Chandan Singh
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  2. Ekta Walia
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Correspondence to Neerja Mittal.

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Singh, C., Walia, E. & Mittal, N. Robust two-stage face recognition approach using global and local features. Vis Comput 28, 1085–1098 (2012). https://doi.org/10.1007/s00371-011-0659-7

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