100006 artpubuab driver oai:ddd.uab.cat:100006 articleid 15775097v11n1p41 eng Huang, Hung-Fu National Cheng Kung University (Taiwan). Department of Electrical Engineering Facial Expression Recognition Using New Feature Extraction Algorithm This paper proposes a method for facial expression recognition. Facial feature vectors are generated from keypoint descriptors using Speeded-Up Robust Features. Each facial feature vector is then normalized and next the probability density function descriptor is generated. The distance between two probability density function descriptors is calculated using Kullback Leibler divergence. Mathematical equation is employed to select certain practicable probability density function descriptors for each grid, which are used as the initial classification. Subsequently, the corresponding weight of the class for each grid is determined using a weighted majority voting classifier. The class with the largest weight is output as the recognition result. The proposed method shows excellent performance when applied to the Japanese Female Facial Expression database. Tots els drets reservats http://www.europeana.eu/rights/rr-f/ Anglès Article de fons Speeded-Up Robust Features Probability density function Kullback Leibler Divergence Weighted majority voting info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Tai, Shen-Chuan National Cheng Kung University (Taiwan). Department of Electrical Engineering Vol. 11, Núm. 1 (2012), p. 41-54 ELCVIA : Electronic Letters on Computer Vision and Image Analysis 1577-5097 14 335004 http://ddd.uab.cat/pub/elcvia/elcvia_a2012v11n1/elcvia_a2012v11n1p41.pdf 0041 54 1 11 elcvia_a2012v11n1 2012 ARTPUB ELCVIA UAB file 0 MD5 933dc7349312b5accc38bb910153d83d 335004 bytestream 1.5 filepath pub/elcvia/elcvia_a2012v11n1/elcvia_a2012v11n1p41.pdf disk