Facial Expression Recognition by Automatic Facial Parts Position Detection with Boosted-LBP

Different facial expressions are related to a small set of muscles and limited ranges of motions. In this paper we propose an automatic facial expression recognition system, different from other automatic methods in both face detection and feature extraction. In system the facial expressions identify itself in video sequences. First, the differences between neutral and emotional states are detected. So as automatically locate faces and the facial organs which changes. Region-based method to extract LBP features is applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE database and obtains better recognition rate than other automatic or manual annotated systems.

[1]  Shu Liao,et al.  Facial Expression Recognition using Advanced Local Binary Patterns, Tsallis Entropies and Global Appearance Features , 2006, 2006 International Conference on Image Processing.

[2]  Enrique Muñoz,et al.  Recognising facial expressions in video sequences , 2007, Pattern Analysis and Applications.

[3]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System , 2005, Machine Vision and Applications.

[4]  Bernd Radig,et al.  A real time system for model-based interpretation of the dynamics of facial expressions , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

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

[6]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Dimitrios I. Fotiadis,et al.  A Region Based Methodology for Facial Expression Recognition , 2008, BIOSIGNALS.

[8]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure , 2003, ICVS.

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[10]  Matti Pietikäinen,et al.  A discriminative feature space for detecting and recognizing faces , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[11]  Richard Bowden,et al.  Automatic Facial Expression Recognition Using Boosted Discriminatory Classifiers , 2007, AMFG.

[12]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[13]  Yajie Tian,et al.  Handbook of face recognition , 2003 .

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

[15]  Guodong Guo,et al.  Learning from examples in the small sample case: face expression recognition , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  M. Pietikäinen,et al.  Facial Expression Recognition with Local Binary Patterns and Linear Programming 1 , 2005 .

[17]  Maja Pantic,et al.  Non-rigid registration using free-form deformations for recognition of facial actions and their temporal dynamics , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[18]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[19]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[20]  P. Ekman,et al.  Facial action coding system: a technique for the measurement of facial movement , 1978 .

[21]  Gwen Littlewort,et al.  Fully Automatic Facial Action Recognition in Spontaneous Behavior , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[22]  Shaogang Gong,et al.  A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[23]  Ian R. Fasel,et al.  A generative framework for real time object detection and classification , 2005, Comput. Vis. Image Underst..