Abstract
This paper proposes a new facial expression recognition method which combines Higher Order Local Autocorrelation (HLAC) features with Weighted PCA. HLAC features are computed at each pixel in the human face image. Then these features are integrated with a weight map to obtain a feature vector. We select the weight by combining statistic method with psychology theory. The experiments on the “CMU-PITTSBURGH AU-Coded Face Expression Image Database” show that our Weighted PCA method can improve the recognition rate significantly without increasing the computation, when compared with PCA.
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Liu, F., Wang, Zl., Wang, L., Meng, Xy. (2005). Facial Expression Recognition Using HLAC Features and WPCA. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_12
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DOI: https://doi.org/10.1007/11573548_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
Online ISBN: 978-3-540-32273-3
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