DocumentCode :
2230606
Title :
Facial Expression Recognition with Marginal Fisher Analysis on Local Binary Patterns
Author :
Ying, Zi-lu ; Cai, Lin-bo
Author_Institution :
Sch. of Inf., Wuyi Univ., Jiangmen, China
fYear :
2009
fDate :
26-28 Dec. 2009
Firstpage :
1250
Lastpage :
1253
Abstract :
A novel approach to facial expression recognition with Marginal Fisher Analysis (MFA) on Local Binary Pattern (LBP) is proposed. 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, MFA algorithm based on Graph Embedding (GE) is applied for dimensionality reduction. Finally, Support Vector Machine (SVM) is used to classify the seven expressions (anger, disgust, fear, happiness, neutral, sadness and surprise) on Japanese Female Facial Expression (JAFFE) database. The maximum facial expression recognition rate of the proposed algorithm reaches to 65.71% for person-independent expression recognition, which is better than LBP + LDA algorithms. The experiment results prove that the facial expression recognition with MFA on LBP is an effective and feasible algorithm.
Keywords :
face recognition; support vector machines; LBP operator; facial expression recognition; graph embedding; local binary patterns; marginal fisher analysis; support vector machine; Binary codes; Face recognition; Feature extraction; Laplace equations; Linear discriminant analysis; Pattern analysis; Pattern recognition; Principal component analysis; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4909-5
Type :
conf
DOI :
10.1109/ICISE.2009.585
Filename :
5455445
Link To Document :
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