A robust feature extraction method for human facial expressions recognition systems

Feature extraction is one of the most important modules for Facial Expression Recognition (FER) systems, which deals with getting the distinguishable features each expression and quantizing it as a discrete symbol. In this paper, we have proposed the novel robust feature extraction technique for the FER systems called Stepwise Linear Discriminant Analysis (SWLDA). This technique focuses on the selection of localized features from the facial expression images and discriminate their classes on the basis of regression values i.e. partial F-test. The proposed technique is then compared with conventional techniques such as LDA in combination with ICA. The results shows that SWLDA better than conventional techniques in terms of robustness in suitable feature selection and classification.

[1]  Friedhelm Schwenker,et al.  A Multiple Classifier System Approach for Facial Expressions in Image Sequences Utilizing GMM Supervectors , 2010, 2010 20th International Conference on Pattern Recognition.

[2]  Ioannis Pitas,et al.  ICA and Gabor representation for facial expression recognition , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[3]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[5]  Tae-Seong Kim,et al.  Human Facial Expression Recognition Using Fisher Independent Component Analysis and Hidden Markov Model , 2009 .

[6]  A. Rogier.  [Communication without words]. , 1971, Tijdschrift voor ziekenverpleging.

[7]  J. Cohn,et al.  Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding. , 1999, Psychophysiology.

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

[9]  Oksam Chae,et al.  Local Directional Pattern (LDP) – A Robust Image Descriptor for Object Recognition , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[10]  Wang Zeng-fu.  An expression space model for facial expression analysis , 2009 .

[11]  Chung-Lin Huang,et al.  Facial Expression Recognition Using Model-Based Feature Extraction and Action Parameters Classification , 1997, J. Vis. Commun. Image Represent..

[12]  Jieyu Zhao,et al.  Curvelet feature extraction for face recognition and facial expression recognition , 2010, 2010 Sixth International Conference on Natural Computation.

[13]  Shahrel Azmin Suandi,et al.  Finger Vein Recognition Using Local Line Binary Pattern , 2011, Sensors.

[14]  Takeo Kanade,et al.  Facial Expression Analysis , 2011, AMFG.

[15]  Fernando De la Torre,et al.  Facial Expression Analysis , 2011, Visual Analysis of Humans.

[16]  Frank Y. Shih,et al.  Recognizing facial action units using independent component analysis and support vector machine , 2006, Pattern Recognit..

[17]  Oksam Chae,et al.  Robust Facial Expression Recognition Based on Local Directional Pattern , 2010 .

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

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

[20]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[21]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.