Abstract  Automatic  facial  expression  recognition  is  a  challe nging  problem  in  computer  vision,  and  has  gained  si gnificant  importance in the applications of human-computer in teractions. The vital component of any successful expression recognition  system  is  an  effective  facial  representation  from  f ace  images.  In  this  paper,  we  have  derived  an  appea rance-based  feature  descriptor,  the  Local  Directional  Pattern  Variance  (LDPv),  which  characterizes  both  the  texture  and  co ntrast  information  of  facial  components.  The  LDPv  descriptor  is  a  collect ion  of  Local  Directional  Pattern  (LDP)  codes  weight ed  by  their  corresponding  variances.  The  feature  dimension  is  t hen  reduced  by  extracting  the  most  discriminative  e lements  of  the  representation  with  Principal  Component  Analysis  (P CA).  The  recognition  performance  based  on  our  LDPv  descriptor  has  been  evaluated  using  Cohn-Kanade  expression  databas e  with  a  Support  Vector  Machine  (SVM)  classifier.  The  discriminative  strength  of  LDPv  representation  is  also  assessed  ov er  a  useful  range  of  low  resolution  images.  Experim ental  results  with  prototypic  expressions  show  that  the  LDPv  descripto r  has  achieved  a  higher  recognition  rate,  as  compared  to  other  existing  appearance-based feature descriptors .    

References

[1] Ahonen T., Hadid A., and Pietikainen 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.

[2] Ahonen T., Hadid A., and Pietik inen M., Face Recognition with Local Binary Patterns, in Proceedings of 8 th European Conference on Computer Vision , Berlin, pp. 469-481, 2004.

[3] Bassili N., Emotion Recognition: The Role of Facial Movement and the Relative Importance of Upper and Lower Area of the Face, Journal of Personality and Social Psychology , vol. 37, no. 11, pp. 2049-2058, 1979.

[4] Bartlett S., 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 Conference on Computer Vision and Pattern Recognition , California, vol. 2, pp. 568- 573, 2005.

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

[6] Brunelli R. and Poggio T., Face Recognition: Features Versus Templates, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 15, no. 10, pp. 1042-1052, 1993. 390 The International Arab Journal of Infor mation Technology, Vol. 9, No. 4, July 2012

[7] Chao-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.

[8] Cohen I., Sebe N., Garg A., Chen L., and Huang T., Facial Expression Recognition from Video Sequences: Temporal and Static Modeling, Computer Vision and Image Understanding , vol. 91, no. 1-2, pp. 160-187, 2003.

[9] Donato G., Bartlett S., Hagar C., Ekman P., and Sejnowski J., Classifying Facial Actions, IEEE Transaction on Pattern Analysis and Machine Intelligence , vol. 21, no. 10, pp. 974-989, 1999.

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

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

[12] Gundimada S. and Asari K., Facial Recognition Using Multisensor Images Based on Localized Kernel Eigen Spaces, IEEE Transactions on Image Processing , vol. 18, no. 6, pp. 1314-1325, 2009.

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

[14] Jabid T., Kabir H., and Chae O., Local Directional Pattern for Face Recognition, in Proceedings of IEEE International Conference on Consumer Electronics , NV, pp. 329-330, 2010.

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

[16] Kumar A., Analysis of Unsupervised Dimensionality Reduction Techniques, Computer Science and Information System s, vol. 6, no. 2, pp. 217-227, 2009.

[17] Lajevardi M. and Hussain M., Feature Extraction for Facial Expression Recognition Based on Hybrid Face Regions, Advances in Electrical and Computer Engineering , vol. 9, no. 3, pp. 63-67, 2009.

[18] Lajevardi M. and Hussain M., Higher Order Orthogonal Moments for Invariant Facial Expression Recognition, Digital Signal Processing , vol. 20, no. 6, pp. 1771-1779, 2010.

[19] Manjunath S. and Ma Y., Texture Features for Browsing and Retrieval of Image Data, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 18, no. 8, pp. 837-842, 1999.

[20] Meulders M., Boeck D., Mechelen V., and Gelman A., Probabilistic Feature Analysis of Facial Perception of Emotions, Applied Statistic s, vol. 54, no. 4, pp. 781-793, 2005.

[21] Ojala T. and Pietikainen M., 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.

[22] Pantic M. and Rothkrantz M., Automatic Analysis of Facial Expressions: the State of the Art, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 22, no. 12, pp. 1424- 1445, 2000.

[23] Qader H., Ramli A., and Haddad S., Fingerprint Recognition Using Zernike Moments, International Arab Journal of Information Technology , vol. 4, no. 4, pp. 372-376, 2007.

[24] Shan C., Gong S., and McOwan P., Robust Facial Expression Recognition Using Local Binary Patterns, in Proceedings of IEEE International Conference Image Processing , pp. 914-917, 2005

[25] 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.

[26] Tian Y., Brown L., Hampapur A., Pankanti S., Senior A., and Bolle R., Real World Real-Time Automatic Recognition of Facial Expressions, in Proceedings of IEEE Workshop on Performance Evaluation of Tracking and Surveillance , USA, pp. 9-16, 2003.

[27] Tian Y., Kanade T., and Cohn J., Facial Expression Analysis: Handbook of Face Recognition , Springer, 2003.

[28] Tian Y., Evaluation of Face Resolution for Expression Analysis, in Proceedings of CVPR Workshop on Face Processing in Video , USA, pp. 82, 2004.

[29] Turk A. and Pentland P., Face Recognition Using Eigenfaces, in Proceedings of Computer Vision and Pattern Recognition , USA, pp. 586- 591, 1991.

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

[31] 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 Conference on Computer Vision and Pattern Recognition Workshop , USA, vol. 3, pp. 76-84, 2005.

[32] Valstar M. and Pantic M., Fully Automatic Facial Action Unit Detection and Temporal Analysis, in Proceedings of IEEE Conference Local Directional Pattern Variance (LDPv): A Robust Feature Descriptor for Facial Expression Recognition 391 on Computer Vision and Pattern Recognition Workshop , New York, pp. 149, 2006.

[33] Zhang Z., Lyons M., Schuster M., and Akamatsu S., Comparison between Geometry-Based and Gabor-Wavelets-Based Facial Expression Recognition using Multi-Layer Perceptron, in Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition , USA, pp. 454-459, 1998.

[34] Zhao G. and Pietikainen M., Boosted Multi- Resolution Spatiotemporal Descriptors for Facial Expression Recognition, Pattern Recognition Letters , vol. 30, no. 12. pp. 1117-1127, 2009.

[35] 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. Hasanul Kabir received his BSc degree in computer science and information technology from the Islamic University of Technology, Bangladesh in 2003, and PhD degree in computer engineering from Kyung Hee University, South Korea in 2011. Currently he is serving as an assistant professor in the Department of CSE in Islamic University of Technology. His research interests include feature extraction, motion estimation, computer vision, and pattern recognition. Taskeed Jabid received his BSc degree in computer science from East West University, Bangladesh in 2001. He worked as a lecturer in the Computer Science and Engineering Department of East West University, Bangladesh. Currently, he is pursuing his PhD degree in the Department of Computer Engineering, Kyung Hee University, South Korea. His research interests include texture analy sis, image processing, computer vision, and pattern recognition. Oksam Chae received his BSc degree in electronics engineering from Inha University, South Korea in 1977. He completed his MS and PhD degree in electrical and computer engineering from Oklahoma State University, USA in 1982 and 1986, respectively. In 1986-88 he worked a s research engineer for Texas Instruments, USA. Since 1988, he has been working as a professor in the Department of Computer Engineering, Kyung Hee University, South Korea. His research interests inc lude multimedia data processing environments, intelligen t filter, motion estimation, intrusion detection syst em, and medical image processing in dentistry. He is a member of IEEE, SPIE, KES and IEICE.