Conference Paper

Contourlet structural similarity for facial expression recognition

Sch. of Electr. & Comput. Eng., RMIT Univ., Melbourne, VIC, Australia
DOI: 10.1109/ICASSP.2010.5495357 Conference: Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Source: IEEE Xplore

ABSTRACT This paper presents a novel classification method based on perceptual image quality metrics for facial expression recognition. The features are extracted based on Contourlet sub-bands. Then, the optimum features are selected using minimum redundancy and maximum relevance algorithm (MRMR). The selected features are classified by structural similarity metric in contourlet domain. The proposed method has been extensively assessed using two different databases: the Cohn-Kanade database and the JAFFE database. A series of experiments have been carried out and a comparative study suggests the efficiency of the proposed method in enhancing the classification rates of a number of known algorithms.

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