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3-D face recognition: features, databases, algorithms and challenges

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Abstract

Face recognition is being widely accepted as a biometric technique because of its non-intrusive nature. Despite extensive research on 2-D face recognition, it suffers from poor recognition rate due to pose, illumination, expression, ageing, makeup variations and occlusions. In recent years, the research focus has shifted toward face recognition using 3-D facial surface and shape which represent more discriminating features by the virtue of increased dimensionality. This paper presents an extensive survey of recent 3-D face recognition techniques in terms of feature detection, classifiers as well as published algorithms that address expression and occlusion variation challenges followed by our critical comments on the published work. It also summarizes remarkable 3-D face databases and their features used for performance evaluation. Finally we suggest vital steps of a robust 3-D face recognition system based on the surveyed work and identify a few possible directions for research in this area.

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  1. Visvesvaraya National Institute of Technology, Nagpur, India

    Hemprasad Patil, Ashwin Kothari & Kishor Bhurchandi

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Patil, H., Kothari, A. & Bhurchandi, K. 3-D face recognition: features, databases, algorithms and challenges. Artif Intell Rev 44, 393–441 (2015). https://doi.org/10.1007/s10462-015-9431-0

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