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Issue No. 03 - July-Sept. (2014 vol. 5)
ISSN: 1949-3045
pp: 340-351
Seung Ho Lee , Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Republic of Korea
Konstantinos N. Kostas Plataniotis , Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
Yong Man Ro , Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Republic of Korea
Automatic facial expression recognition (FER) is becoming increasingly important in the area of affective computing systems because of its various emerging applications such as human-machine interface and human emotion analysis. Recently, sparse representation based FER has become popular and has shown an impressive performance. However, sparse representation could often produce less meaningful sparse solution for FER due to intra-class variation such as variation in identity or illumination. This paper proposes a new sparse representation based FER method, aiming to reduce the intra-class variation while emphasizing the facial expression in a query face image. To that end, we present a new method for generating an intra-class variation image of each expression by using training expression images. The appearance of each intra-class variation image could be close to the appearance of the query face image in identity and illumination. Therefore, the differences between the query face image and its intra-class variation images are used as the expression features for sparse representation. Experimental results show that the proposed FER method has high discriminating capability in terms of improving FER performance. Further, the intra-class variation images of non-neutral expressions are complementary with that of neutral expression, for improving FER performance.
Face, Training, Feature extraction, Lighting, Face recognition, Dictionaries, Emotion recognition

S. H. Lee, K. N. Plataniotis and Y. M. Ro, "Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition," in IEEE Transactions on Affective Computing, vol. 5, no. 3, pp. 340-351, 2014.
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