Facial expression identification system with Euclidean distance of facial edges

In this paper we present facial expression recognition system. Identification and classification is performed on the seven basic expressions: happy, surprise, fear, disgust, sad, anger and a neutral state. This system consists of three main parts. The first step is the detection of the face and facial features to extract the face centered region. Next step consists of a normalization of this interest region and edge extraction. At this step we have a face edge image that we use to calculate the Euclidean distance of all pixels that constitute edges. The third step is the classification of different emotional state by the SVM method.

[1]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[2]  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.

[3]  T. Sejnowski,et al.  Measuring facial expressions by computer image analysis. , 1999, Psychophysiology.

[4]  P. Ekman,et al.  Strong evidence for universals in facial expressions: a reply to Russell's mistaken critique. , 1994, Psychological bulletin.

[5]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[6]  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).

[7]  Sanjay Tanwani,et al.  Recognition of Facial Expressions using Local Binary Patterns of Important Facial Parts , 2013 .

[8]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  M. V. Lamar,et al.  Recognizing facial actions using Gabor wavelets with neutral face average difference , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[10]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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

[14]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[15]  Devi Arumugam,et al.  Emotion Classification Using Facial Expression , 2011 .

[16]  Zheng Zhang.  Feature-Based Facial Expression Recognition: Experiments With a Multi-Layer Perceptron , 1998 .

[17]  Takeo Kanade,et al.  Human Face Detection in Visual Scenes , 1995, NIPS.

[18]  A. Mehrabian.  Communication without words , 1968 .

[19]  Ajay Mittal,et al.  Application of Complete Local Binary Pattern Method for facial expression recognition , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[20]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Maja Pantic,et al.  Expert system for automatic analysis of facial expressions , 2000, Image Vis. Comput..