Spatial Feature Interdependence Matrix (SFIM): A Robust Descriptor for Face Recognition

  • Anbang Yao
  • Shan Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)


In this paper, a new face descriptor called spatial feature interdependence matrix (SFIM) is proposed for addressing representation of human faces under variations of illumination and facial expression. Unlike traditional face descriptors which usually use a hierarchically organized or a sequentially concatenated structure to describe the spatial arrangement of features in different facial regions, SFIM is focused on exploring inherent spatial feature interdependences among separated facial regions in a face image. We compute the feature interdependence strength between each pair of facial regions as the Chi square distance between two corresponding histogram based feature vectors. Once face images are represented as SFIMs, we then employ spectral regression discriminant analysis (SRDA) to achieve face recognition under a nearest neighbor search framework. Extensive experimental results on two well-known face databases demonstrate that the proposed method has superior performance in comparison with related approaches.


Face recognition spatial feature interdependence matrix spectral regression discriminant analysis object representation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anbang Yao
    • 1
  • Shan Yu
    • 2
  1. 1.National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of ScienceBeijingChina
  2. 2.National Institute for Research in Computer Science and ControlFrance

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