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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Facial Expression Recognition with Local Binary Pattern and Laplacian Eigenmaps
verfasst von : Zilu Ying, Linbo Cai, Junying Gan, Sibin He
Erschienen in: Emerging Intelligent Computing Technology and Applications
Verlag: Springer Berlin Heidelberg
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A new approach to facial expression recognition is constructed by combining the Local Binary Pattern and Laplacian Eigenmaps. Firstly, each image is transformed by an LBP operator and then divided into 3×5 non-overlapping blocks. The features of facial expression images are formed by concatenating the LBP histogram of each block. Secondly, linear graph embedding framework is used as a platform, and then Laplacian Eigenmaps is developed under this framework and applied for feature dimensionality reduction. Finally, Support Vector Machine is used to classify the seven expressions (anger, disgust, fear, happiness, neutral, sadness and surprise) on JAFFE database. The maximum facial expression recognition rate of the proposed algorithm reaches to 70.48% for person-independent recognition, which is much better than that of LBP+PCA and LBP+LDA algorithms. The experiment results prove that the facial expression recognition with local binary pattern and Laplacian Eigenmaps is an effective and feasible algorithm.