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Attention control with reinforcement learning for face recognition under partial occlusion

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Abstract

In this paper a new method for handling occlusion in face recognition is presented. In this method the faces are partitioned into blocks and a sequential recognition structure is developed. Then, a spatial attention control strategy over the blocks is learned using reinforcement learning. The outcome of this learning is a sorted list of blocks according to their average importance in the face recognition task. In the recall mode, the sorted blocks are employed sequentially until a confident decision is made. Obtained results of various experiments on the AR face database demonstrate the superior performance of proposed method as compared with that of the holistic approach in the recognition of occluded faces.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Control and Intelligent Processing Center of Excellence, University of Tehran, Tehran, Iran

    Ehsan Norouzi, Majid Nili Ahmadabadi & Babak Nadjar Araabi

  2. School of Cognitive Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Majid Nili Ahmadabadi & Babak Nadjar Araabi

Authors
  1. Ehsan Norouzi
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  2. Majid Nili Ahmadabadi
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  3. Babak Nadjar Araabi
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Correspondence to Ehsan Norouzi.

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Norouzi, E., Nili Ahmadabadi, M. & Nadjar Araabi, B. Attention control with reinforcement learning for face recognition under partial occlusion. Machine Vision and Applications 22, 337–348 (2011). https://doi.org/10.1007/s00138-009-0235-6

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  • DOI: https://doi.org/10.1007/s00138-009-0235-6

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