Spontaneous Facial Expression Recognition Based on Histogram of Oriented Gradients Descriptor

Automatically detecting facial expressions has become an important research area. It plays a significant role in security, human-computer interaction and health-care. Yet, earlier work focuses on posed facial expression. In this paper, we propose a spontaneous facial expression recognition method based on effective feature extraction and facial expression recognition for Micro Expression analysis. In feature extraction we used histogram of oriented gradients (HOG) descriptor to extract facial expression features. Expression recognition is performed by using a Support vector machine (SVM) classifier to recognize six emotions (happiness, anger, disgust, fear, sadness and surprise). Experiments show promising results of the proposed method with recognition accuracy of 95% on static images while 80% on videos.

[1]  Ravindra C. Thool,et al.  Automatic facial feature extraction and expression recognition based on neural network , 2012, ArXiv.

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

[3]  K. Ramar,et al.  A Neuro Fuzzy approach for Facial Expression Recognition using LBP Histograms , 2010 .

[4]  A. Laidlaw,et al.  Micro-expression recognition training in medical students: a pilot study , 2009, BMC medical education.

[5]  P. Ekman.  Facial expressions of emotion: an old controversy and new findings. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[6]  Yuxiao Hu,et al.  Spontaneous Emotional Facial Expression Detection , 2006, J. Multim..

[7]  C. Darwin.  The Expression of the Emotions in Man and Animals , .

[8]  Roger Lee.  Computer and Information Science 2011 , 2011 .

[9]  Dmitry B. Goldgof,et al.  Macro- and micro-expression spotting in long videos using spatio-temporal strain , 2011, Face and Gesture 2011.

[10]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Jesse S. Jin,et al.  Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces , 2004, VIP.

[12]  Xiaolan Fu,et al.  Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine , 2014, Neural Processing Letters.

[13]  Takio Kurita,et al.  Selection of Histograms of Oriented Gradients Features for Pedestrian Detection , 2007, ICONIP.

[14]  Paul Ekman,et al.  Lie Catching and Microexpressions , 2009 .

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

[16]  Adrian Kaehler,et al.  Learning opencv, 1st edition , 2008 .

[17]  Aliaa A. A. Youssif,et al.  Automatic Facial Expression Recognition System Based on Geometric and Appearance Features , 2011, Comput. Inf. Sci..

[18]  L. Fleischer.  Telling Lies Clues To Deceit In The Marketplace Politics And Marriage , 2016 .

[19]  P. Ekman.  Telling lies: clues to deceit in the marketplace , 1985 .

[20]  Matti Pietikäinen,et al.  A Spontaneous Micro-expression Database: Inducement, collection and baseline , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[21]  Alex Pentland,et al.  Facial expression recognition using a dynamic model and motion energy , 1995, Proceedings of IEEE International Conference on Computer Vision.

[22]  Alex Pentland,et al.  LAFTER: a real-time face and lips tracker with facial expression recognition , 2000, Pattern Recognit..