Hybrid-boost learning for multi-pose face detection and facial expression recognition

This paper proposes a hybrid-boost learning algorithm for multi-pose face detection and facial expression recognition. To speed-up the detection process, the system searches the entire frame for the potential face regions by using skin color detection and segmentation. Then it scans the skin color segments of the image and applies the weak classifiers along with the strong classifier for face detection and expression classification. This system detects human face in different scales, various poses, different expressions, partial-occlusion, and defocus. Our major contribution is proposing the weak hybrid classifiers selection based on the Harr-like (local) features and Gabor (global) features. The multi-pose face detection algorithm can also be modified for facial expression recognition. The experimental results show that our face detection system and facial expression recognition system have better performance than the other classifiers.

[1]  Aggelos K. Katsaggelos,et al.  Automatic facial expression recognition using facial animation parameters and multistream HMMs , 2006, IEEE Transactions on Information Forensics and Security.

[2]  Alexandre Bernardino,et al.  Gabor Parameter Selection for Local Feature Detection , 2005, IbPRIA.

[3]  Michael E. Tipping.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[4]  Xun Xu,et al.  Face recognition with MRC-boosting , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Tyng-Luh Liu,et al.  Robust face detection with multi-class boosting , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Paul A. Viola,et al.  Fast Multi-view Face Detection , 2003 .

[7]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Javier Ruiz-del-Solar,et al.  A Hybrid Face Detector Based on an Asymmetrical Adaboost Cascade Detector and a Wavelet-Bayesian-Detector , 2003, IWANN.

[10]  Allen R. Hanson,et al.  Feature Selection Using Adaboost for Face Expression Recognition , 2005 .

[11]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[12]  Léon J. M. Rothkrantz,et al.  Facial Expression Recognition with Relevance Vector Machines , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[13]  Gwen Littlewort,et al.  Recognizing facial expression: machine learning and application to spontaneous behavior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Abdesselam Bouzerdoum,et al.  Skin color detection for face localization in human-machine communications , 2001, Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467).

[15]  Guodong Guo,et al.  Boosting for fast face recognition , 2001, Proceedings IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems.

[16]  Mingjing Li,et al.  Robust multipose face detection in images , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Yuan Li,et al.  Vector boosting for rotation invariant multi-view face detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[18]  Kah Bin Lim,et al.  Comparison of the support vector machine and relevant vector machine in regression and classification problems , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[19]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

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

[21]  Bo Wu,et al.  Real time facial expression recognition with AdaBoost , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[22]  King Ngi Ngan,et al.  Face segmentation using skin-color map in videophone applications , 1999, IEEE Trans. Circuits Syst. Video Technol..

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

[24]  Myung Jin Chung,et al.  Efficient rectangle feature extraction for real-time facial expression recognition based on AdaBoost , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  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.

[27]  Stan Z. Li,et al.  FloatBoost learning and statistical face detection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Beat Fasel,et al.  Automatic facial expression analysis: a survey , 2003, Pattern Recognit..