Automatic 3D face recognition from depth and intensity Gabor features

  • Authors:

  • Chenghua Xu;Stan Li;Tieniu Tan;Long Quan

  • Affiliations:

  • National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing 100080, PR China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing 100080, PR China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing 100080, PR China;Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

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Abstract

As is well known, traditional 2D face recognition based on optical (intensity or color) images faces many challenges, such as illumination, expression, and pose variation. In fact, the human face generates not only 2D texture information but also 3D shape information. In this paper, we investigate what contributions depth and intensity information makes to face recognition when expression and pose variations are taken into account, and we propose a novel system for combining depth and intensity information to improve face recognition systems. In our system, local features described by Gabor wavelets are extracted from depth and intensity images, which are obtained from 3D data after fine alignment. Then a novel hierarchical selecting scheme embedded in linear discriminant analysis (LDA) and AdaBoost learning is proposed to select the most effective and most robust features and to construct a strong classifier. Experiments are performed on the CASIA 3D face database and the FRGC V2.0 database, two data sets with complex variations, including expressions, poses and long time lapses between two scans. Experimental results demonstrate the promising performance of the proposed method. In our system, all processes are performed automatically, thus providing a prototype of automatic face recognition combining depth and intensity information.