Attention control with reinforcement learning for face recognition under partial occlusion

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.

[1]  Thomas Serre,et al.  A Component-based Framework for Face Detection and Identification , 2007, International Journal of Computer Vision.

[2]  Ioannis Pitas,et al.  An analysis of facial expression recognition under partial facial image occlusion , 2008, Image Vis. Comput..

[3]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[4]  Sang Uk Lee,et al.  Occlusion invariant face recognition using selective local non-negative matrix factorization basis images , 2008, Image Vis. Comput..

[5]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[6]  A. Martínez The AR face databasae , 1998 .

[7]  Jongsun Kim,et al.  Effective representation using ICA for face recognition robust to local distortion and partial occlusion , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Kazuhiro Hotta Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel , 2008, Image Vis. Comput..

[9]  Majid Nili Ahmadabadi,et al.  Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition , 2009, International Journal of Computer Vision.

[10]  Monson H. Hayes,et al.  An embedded HMM-based approach for face detection and recognition , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[11]  Albert Ali Salah,et al.  A Selective Attention-Based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[14]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 2005, IEEE Transactions on Neural Networks.

[15]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[16]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[17]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[18]  Witold Pedrycz,et al.  Face recognition: A study in information fusion using fuzzy integral , 2005, Pattern Recognit. Lett..

[19]  Majid Nili Ahmadabadi,et al.  Cost-sensitive learning of top-down modulation for attentional control , 2009, Machine Vision and Applications.

[20]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..