Abstract Gabor features are successfully applied to solve the problems of facial expression recognition. However, the dimension of Gabor features is usually too high to be practically applicable. A method based on Pyramid Histogram of Oriented Gradients (PHOG) feature and Clustering Linear Discriminate Analysis (CLDA) is proposed for smile expression recognition. The main merits of the proposed system are that the complexity can be decreased with low-dimension PHOG feature, and the multi-model problem can be overcome by CLDA. The experimental results show that system with PHOG feature achieves competitive or even higher recognition accuracy than with the Gabor feature, but with much lower of computation time cost. Moreover, the performance of CLDA does not be degraded significantly when decreasing the feature dimension.
[1] Pantic M,Rothkranz L J M.Expert System for Automatic Analysis of Facial Expression.Image and Computing,2000,18(11): 881-905 [2] Ekman P,Friesen W V,O′Sullivan M,et al.Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion.Journal of Personality and Social Psychology,1987,53(4): 712-717 [3] Valstra M F,Gunes H,Pantic M.How to Distinguish Posed from Spontaneous Smiles Using Geometric Features // Proc of the 9th International Conference on Multimodal Interfaces.Nagoya,Japan,2007: 38-45 [4] Whitehill J,Littlewort G,Fasel I,et al.Toward Practical Smile Detection.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(11): 2106-2111 [5] Lu Guanming,Li Xiaonan,Li Haibo.Research on Recognition for Facial Expression of Pain in Neonates.Acta Optica Sinica,2008,28(11): 2109-2114 (in Chinese) (卢官明,李晓南,李海波.新生儿疼痛面部表情识别方法的研究.光学学报,2008,28(11): 2109-2114) [6] Zeng Zhihong,Pantic M,Roisman G I,et al.A Survey of Affect Recognition Methods: Audio,Visual and Spontaneous Expressions.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(1): 39-58 [7] Littlewort G,Bartlett M S,Fasel I,et al.Dynamics of Facial Expression Extracted Automatically from Video // Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA,2006: 80-81 [8] Whitehill J,Omlin C W.Harr Features for FACS AU Recognition // Proc of the 7th IEEE International Conference on Automatic Face and Gesture Recognition.Southampton,UK,2006: 97-101 [9] Duda R O,Hart P E,Stork D G.Pattern Classification.New York,USA: John Wiley Sons,2001 [10] Kanade T,Cohn J,Tian Yingli.Comprehensive Database for Facial Expression Analysis // Proc of the 4th IEEE International Conference on Face and Gesture Recognition.Grenoble,France,2000: 46-53 [11] Bosch A,Zisserman A,Munoz X.Representing Shape with a Spatial Pyramid Kernel // Proc of the 6th ACM International Conference on Image and Video Retrieval.Amsterdam,Netherlands,2007: 401-408 [12] Tao Dacheng,Li Xuelong,Wu Xindong,et al.Geometric Mean for Subspace Selection.IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(2): 260-274 [13] Bosch A,Zisserman A,Muoz X.Scene Classification Using a Hybrid Generative/Discriminative Approach.IEEE Trans on Pattern Analysis and Machine Intelligence,2008,30(4): 712-727 [14] Yu Hua,Yang Jie.A Direct LDA Algorithm for High Dimensional Data with Application to Face Recognition.Pattern Recognition,2001,34(10): 2067-2070 [15] He Zhijie,Jin Lianwen.A New Fast Training Algorithm for SVM // Proc of the International Conference on Machine Learning and Cybernetics.Kunming,China,2008: 3451-3456
2012, Vol. 25 
