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State-of-the-Art LBP Descriptor for Face Recognition

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Local Binary Patterns: New Variants and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 506))

Abstract

Because of a large variability in facial appearance of the same person and small sample size for each person under adverse conditions, face recognition is very challenging. In general, a suitable face representation and a powerful classifier must be designed to deal with these challenges. In this chapter, local face representations using various ordinal contrast measures were introduced. Many different types of visual features derived from these measures were overviewed. Depending on the type of application, classifiers using different prior information for face matching were described. Furthermore, some of the ideas suggested to overcome the problem of pose variation were summarised. Finally, we compare some of the remarkable state-of-art systems on FERET and LFW databases with their standard protocols. The reported work offers some insights into the merits of various face representation and classifier methods, as well as their role in dealing with the challenges.

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Authors and Affiliations

  1. The Center for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK

    Chi ho Chan, Josef Kittler & Norman Poh

Authors
  1. Chi ho Chan
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  2. Josef Kittler
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  3. Norman Poh
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Correspondence to Chi ho Chan .

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Editors and Affiliations

  1. Computer Information Systems, Missouri State University, Springfield, USA

    Sheryl Brahnam

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. Departimento di Elettronica e Informatica (DEI), Università di Padova, Padua, Italy

    Loris Nanni

  4. DEIS University of Bologna, Cesena, Italy

    Alessandra Lumini

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Chan, C.h., Kittler, J., Poh, N. (2014). State-of-the-Art LBP Descriptor for Face Recognition. In: Brahnam, S., Jain, L., Nanni, L., Lumini, A. (eds) Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol 506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39289-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-39289-4_9

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