Abstract  Automatic recognition of facial expression is an ac tive research topic in computer vision due to its importance in both human$computer and social interaction. One of the critical issues for a successful facial expression recognition system is to design a robust facial feature descriptor. Among the different existing methods, the Local Binary Pattern (LBP) has been proved to be a simple and effective one for facial expression representation. However, the LBP method thresholds P neighbors exactly at the value of the center pixel in a local neighborhood and encodes only the signs of the dif ferences between the gray values. Thus, it loses some important texture infor mation. In this paper, we present a robust facial f eature descriptor constructed with the Compound Local Binary Pattern (CLBP) for person$independent facial expression recognition, which overcomes the limitations of LBP. The proposed CLBP operator combines extra P bits with the original LBP code in order to construct a robust feature descriptor that exploits both the sign and the magnitude information of the differences between the center and the neighbor gray values. The recognitio n performance of the proposed method is evaluated u sing the Cohn$ Kanade (CK) and the Japanese Female Facial Expressi on (JAFFE) database with a Support Vector Machine (SVM) classifier. Experimental results with prototypic expressions sh ow the superiority of the CLBP feature descriptor a gainst some well$ known appearance$based feature representation metho ds.   

References

[1] Ahmed F., Hossain E., Bari A., and Shihavuddin A., Compound Local Binary Pattern for Robust Facial Expression Recognition, in Proceedings of the 12 th IEEE International Symposium on Computational Intelligence and Informatics , Hungary, pp. 391-395, 2011.

[2] Ahmed F. and Kabir M., Directional Ternary Pattern for Facial Expression Recognition, in Proceedings of the IEEE International Conference on Consumer Electronics , Las Vegas, pp. 265-266, 2012.

[3] Ahonen T., Hadid A., and Pietkainen M., Face Description with Local Binary Patterns: Application to Face Recognition, IEEE Transaction on Pattern Analysis and Machine Intelligence , vol. 28, no. 12, pp. 2037-2041, 2006.

[4] Bartlett M., Littlewort G., Frank M., Lainscsek C., Fasel I., and Movellan J., Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition , San Diego, vol. 2, pp. 568-573, 2005.

[5] Bartlett M., Movellan J., and Sejnowski T., Face Recognition by Independent Component Analysis, IEEE Transaction on Neural Networks , vol. 13, no. 6, pp. 1450-1464, 2002.

[6] Donato G., Bartlett M., Hager J., Ekman P., and Sejnowski T., Classifying Facial Actions, 202 The International Arab Journal of Information Te chnology, Vol. 11, No. 2, March 2014 IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 974- 989, 1999.

[7] Ekman P. and Friesen W., Facial Action Coding System: A Technique for Measurement of Facial Movement , Consulting Psychologists Press, USA, 1978.

[8] Fa C. and Shin F., Recognizing Facial Action Units using Independent Component Analysis and Support Vector Machine, Pattern Recognition, vol. 39, no. 9, pp. 1795-1798, 2006.

[9] Fasel B. and Luettin J., Automatic Facial Expression Analysis: A Survey, Pattern Recognition , vol. 36, no. 1, pp. 259-275, 2003.

[10] Gundimada S. and Asari V., Facial Recognition using Multisensor Images Based on Localized Kernel Eigen Spaces, IEEE Transaction on Image Processing , vol. 18, no. 6, pp. 1314-1325, 2009.

[11] Guo G. and Dyer C., Simultaneous Feature Selection and Classi er Training via Linear Programming: A Case Study for Face Expression Recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , USA, vol. 1, pp. 346-352, 2003.

[12] He D. and Cercone N., Local Triplet Pattern for Content-based Image Retrieval, in Proceedings of the 6 th International Conference Image Analysis and Recognition Lecture Notes in Computer Science , vol. 5627, pp. 229-238, 2009.

[13] Hsu C. and Lin C., A Comparison on Methods for Multiclass Support Vector Machines, IEEE Transaction on Neural Networks , vol. 13, no. 2, pp. 415-425, 2002.

[14] Jabid T., Kabir M., and Chae O., Robust Facial Expression Recognition Based on Local Directional Pattern, Electronics and Telecommunications Research Institute Journal , vol. 32, no. 5, pp. 784-794, 2010.

[15] Kabir H., Jabid T., and Chae O., Local Directional Pattern Variance: A Robust Feature Descriptor for Facial Expression Recognition, the International Arab Journal of Information Technology , vol. 9, no. 4, pp. 382-391, 2012.

[16] Kanade T., Cohn J., and Tian Y., Comprehensive Database for Facial Expression Analysis, in Proceedings of the IEEE International Conference on Automated Face and Gesture Recognition , Grenoble, pp. 46-53, 2000.

[17] Lyons M., Budynek J., and Akamatsu S., Automatic Classification of Single Facial Images, IEEE Transaction on Pattern Analysis and Machine Intelligence , vol. 21, no. 12, pp. 1357-1362, 1999.

[18] Ojala T., Pietikainen M., and Maenpaa T., Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transaction on Pattern Analysis and Machine Intelligence , vol. 24, no. 7, pp. 971-987, 2002.

[19] Padgett C. and Cottrell G., Representation Face Images for Emotion Classification, in Proceedings of Advances in Neural Information Processing Systems , USA, vol. 9, pp. 1-8, 1996.

[20] Shan C., Gong S., and McOwan P., Facial Expression Recognition Based on Local Binary Patterns: A Comprehensive Study, Image and Vision Computing , vol. 27, no. 6, pp. 803-816, 2009.

[21] Tan X. and Triggs B., Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions, in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures , Brazil, vol. 4778, pp. 168-182, 2007.

[22] Tian Y., Evaluation of Face Resolution for Expression Analysis, in Proceedings of Computer Vision and Pattern Recognition Workshop , Washington, pp. 1-7, 2004.

[23] Tian Y., Kanade T., and Cohn J., Facial Expression Analysis , Springer, New York, 2005.

[24] Uddin Z., Lee J., and Kim T., An Enhanced Independent Component-Based Human Facial Expression Recognition from Video, IEEE Transaction on Consumer Electronics , vol. 55, no. 4, pp. 2216-2224, 2009.

[25] Valstar M. and Pantic M., Fully Automatic Facial Action Unit Detection and Temporal Analysis, in Proceedings of IEEE Computer Vision and Pattern Recognition Workshop , New York, pp. 149, 2006.

[26] Valstar M., Patras I., and Pantic M., Facial Action Unit Detection using Probabilistic Actively Learned Support Vector Machines on Tracked Facial Point Data, in Proceedings of IEEE Computer Vision and Pattern Recognition$ Workshops , San Diego, vol. 3, pp. 76-84, 2005.

[27] Zhang Z., Feature-Based Facial Expression Recognition: Sensitivity Analysis and Experiment with a Multi-Layer Perceptron, International Journal of Pattern Recognition and Artificial Intelligence , vol. 13, no. 6, pp. 893-911, 1999.

[28] Zhao W., Chellappa R., and Phillips P., Face Recognition: A Literature Survey, ACM Computing Survey , vol. 35, no. 4, pp. 399-458, 2003.

[29] Zhou H., Wang R., and Wang C., A Novel Extended Local Binary Pattern Operator for Texture Analysis, Information Sciences , vol. 178, no. 22, pp. 4314-4325, 2008. Person$Independent Facial Expression Recognition Based on Compound 203 Faisal Ahmed received his BSc degree in computer science and information technology from the Islamic University of Technology, Bangladesh in 2010. He is now working as a lecturer in the Department of CSE, Islamic University of Technology, Bangladesh. His research interests include texture analysis, computer vision , and pattern recognition. Hossain Bari received his BSc degree in computer science and information technology from the Islamic University of Technology, Bangladesh in 2010. He is now working in the Samsung Bangladesh R & D Center Ltd, Bangladesh. His research interests include image processing and computer vision. Emam Hossain received his BSc degree in computer science and information technology from the Islamic University of Technology, Bangladesh in 2010. He is now working as a lecturer in the Department of CSE, Ahsanullah University of Science and Technology, Bangladesh. H is research interests include image processing, comput er vision, artificial intelligence, and pattern recogn ition.