A Novel Automatic Facial Expression Recognition Method Based on AAM

This paper proposes anovel method to recognize facial expression through ActiveAppearance Model (AAM)to extract facial regions based on Facial Action CodingSystem (FACS). Itis composed of three parts: extractionof facial regions based on AAM,extraction of facial featuresby Gabor wavelettransformation, and expressionrecognition through Support Vector Machines (SVMs).AAM has better performance thanother methodsin eliminations of the influenceof different facialregion size, head pose and lighting condition and thus can effectively increase the recognitionaccuracy. Therefore it is usedto extract facial regions before extracting features by Gabor wavelettransformation. Finally, SVMsis appliedto recognize expression for its advantage of solvingthe problems of small sample size and overfitting. The feasibility and effectiveness of this method are evaluated and verified by experiments, and satisfactoryresults are achieved.

[1]  Daijin Kim,et al.  Natural facial expression recognition using differential-AAM and manifold learning , 2009, Pattern Recognit..

[2]  Hamed Shah-Hosseini,et al.  A novel fuzzy facial expression recognition system based on facial feature extraction from color face images , 2012, Eng. Appl. Artif. Intell..

[3]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[4]  Oksam Chae,et al.  Local Directional Number Pattern for Face Analysis: Face and Expression Recognition , 2013, IEEE Transactions on Image Processing.

[5]  Gwen Littlewort,et al.  Learning spatiotemporal features by using independent component analysis with application to facial expression recognition , 2012, Neurocomputing.

[6]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[7]  Timothy F. Cootes,et al.  Statistical models of face images - improving specificity , 1998, Image Vis. Comput..

[8]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[9]  Y. V. Venkatesh,et al.  Facial expression recognition using radial encoding of local Gabor features and classifier synthesis , 2012, Pattern Recognit..

[10]  Chengjun Liu,et al.  Gabor-based kernel PCA with fractional power polynomial models for face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Maja Pantic,et al.  Automatic Analysis of Facial Expressions: The State of the Art , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Jing Xiao,et al.  Multi-View AAM Fitting and Construction , 2008, International Journal of Computer Vision.

[13]  Oksam Chae,et al.  Local Directional Pattern (LDP) for face recognition , 2010, 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE).

[14]  Ioannis Pitas,et al.  Comparison of ICA approaches for facial expression recognition , 2009, Signal Image Video Process..

[15]  Qijun Zhao,et al.  Facial expression recognition on multiple manifolds , 2011, Pattern Recognit..

[16]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[17]  Franck Davoine,et al.  Facial expression recognition and synthesis based on an appearance model , 2004, Signal Process. Image Commun..

[18]  Gwen Littlewort,et al.  Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction. , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[19]  Yi Ji,et al.  Automatic facial expression recognition based on spatiotemporal descriptors , 2012, Pattern Recognit. Lett..

[20]  Tsuhan Chen,et al.  Reinterpreting the Application of Gabor Filters as a Manipulation of the Margin in Linear Support Vector Machines , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Luiz Eduardo Soares de Oliveira,et al.  Fusion of feature sets and classifiers for facial expression recognition , 2013, Expert Syst. Appl..

[22]  Ioannis Pitas,et al.  Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines , 2007, IEEE Transactions on Image Processing.

[23]  Hideyuki Sawada,et al.  Extraction of the minimum number of Gabor wavelet parameters for the recognition of natural facial expressions , 2011, Artificial Life and Robotics.

[24]  J LyonsMichael,et al.  Automatic Classification of Single Facial Images , 1999 .

[25]  Tsuhan Chen,et al.  The painful face - Pain expression recognition using active appearance models , 2009, Image Vis. Comput..

[26]  Alex Pentland,et al.  Coding, Analysis, Interpretation, and Recognition of Facial Expressions , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Timothy F. Cootes,et al.  Interpreting face images using active appearance models , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[28]  Zahir M. Hussain,et al.  Automatic facial expression recognition: feature extraction and selection , 2010, Signal, Image and Video Processing.

[29]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[30]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[31]  Marian Stewart Bartlett,et al.  Classifying Facial Actions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  P PentlandAlex,et al.  Coding, Analysis, Interpretation, and Recognition of Facial Expressions , 1997 .

[33]  Luiz Eduardo Soares de Oliveira,et al.  2D Principal Component Analysis for Face and Facial-Expression Recognition , 2011, Computing in Science & Engineering.

[34]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.