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Local Representation of Facial Features

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Book cover Handbook of Face Recognition

Abstract

Feature extraction is one of the fundamental tasks in computer vision and image processing. Respectively, the task of selecting the best set of features to describe faces for recognition, verification, localization, or detection, is a fundamental problem in face biometrics. In this chapter, we review the most popular and successful features for face biometrics. In general, one should include complete algorithms when comparing the features, but certain extraction methods seem to maintain popularity due to their continuous success in various methods and approaches in biometrics and other fields of computer vision and image processing. This chapter specifically describes in more details two prominent local facial features, the first one based on Gabor filter responses, and the second on more recently proposed local binary patterns (LBPs).

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Acknowledgements

Abdenour Hadid and Matti Pietikäinen thank the Academy of Finland for the financial support.

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Authors and Affiliations

  1. Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Lappeenranta, Finland

    Joni-Kristian Kämäräinen

  2. Machine Vision Group, Dept. of Electrical and Information Engineering, University of Oulu, Oulu, Finland

    Abdenour Hadid & Matti Pietikäinen

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  1. Joni-Kristian Kämäräinen
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  2. Abdenour Hadid
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  3. Matti Pietikäinen
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Correspondence to Joni-Kristian Kämäräinen .

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  1. Institute of Automation, Center Biometrics Research & Security, Chinese Academy of Science, Room 1227, Zhongguancun 95, Beijing Donglu, 100080, China, People's Republic

    Stan Z. Li

  2. Dept. Computer Science & Engineering, Michigan State University, East Lansing, 48824-1226, Michigan, USA

    Anil K. Jain

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Kämäräinen, JK., Hadid, A., Pietikäinen, M. (2011). Local Representation of Facial Features. In: Li, S., Jain, A. (eds) Handbook of Face Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-932-1_4

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