This paper proposes asystem forthe facial expression recognition. Firstly, we perform noise reduction by a median filter of facial expression image. Then, a cross-correlation of optical flow and mathematical models from the facial points are used. To define these facial points of interest in the first frame of an input face sequence image, which utilize manually marker. The facial points were automatically tracked by a cross-correlation, which is based on optical flow,and then extracted the feature vectors. The mathematical model extracts features from the feature vectors. An ELMAN neural network was applied to classify expressions. The performances of the proposed facial expressions recognition were computed by Cohn-Kanade facial expressions database. This proposed approach achieved a high recognition rate. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Tai, S., & Huang, H. (2009). Facial expression recognition in video sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5553 LNCS, pp. 1026–1033). https://doi.org/10.1007/978-3-642-01513-7_113
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