A Neural Network Based Facial Expression Analysis using Gabor Wavelets

Facial expression analysis is rapidly becoming an area of intense interest in computer science and human-computer interaction design communities. The most expressive way humans display emotions is through facial expressions. In this paper we present a method to analyze facial expression from images by applying Gabor wavelet transform (GWT) and Discrete Cosine Transform (DCT) on face images. Radial Basis Function (RBF) Network is used to classify the facial expressions. As a second stage, the images are preprocessed to enhance the edge details and non uniform down sampling is done to reduce the computational complexity and processing time. Our method reliably works even with faces, which carry heavy expressions. Keywords— Face Expression, Radial Basis Function, Gabor Wavelet Transform, Human Computer Interaction.

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

[2]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[3]  Guang Yang,et al.  A New Facial Expression Analysis System Based on Warp Image , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[4]  Takeo Kanade,et al.  Meticulously Detailed Eye Region Model , 2008 .

[5]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[7]  Lianwen Jin,et al.  A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA , 2006 .

[8]  Shuzhi Sam Ge,et al.  Face recognition by applying wavelet subband representation and kernel associative memory , 2004, IEEE Transactions on Neural Networks.

[9]  Asahidai Tatsunokuchi Ishikawa FACIAL EXPRESSION ANALYSIS BY KERNEL EIGENSPACE METHOD BASED ON CLASS FEATURES (KEMC) USING NON-LINEAR BASIS FOR SEPARATION OF EXPRESSION-CLASSES , 2004 .

[10]  Shaogang Gong,et al.  A Multi-View Nonlinear Active Shape Model Using Kernel PCA , 1999, BMVC.