Proposed Approach of Detecting Facial Emotion using Neural Network and Representational of HOG Features

The subject of emotion detection from digital image has gained exceptional importance in recent years due to the expansion of visual applications in variowefields of life. With respect to the emotion of human face, the matter becomes more complex according to its variety. At the same time, this matter becomes easier if the computerized technique is used to learn most known emotions and then detect it in the final imaging system. In this paper, a new approach for detecting emotion of human face has been proposed using artificial neural network (ANN). This network is feed by a set of histogram of gradient (HOG) features, as a representative reference to describe the entire emotion. The determining of HOG features is limited to specific region of the face within the digital image. This region is designed to take T shape which covers main parts of human face (eye, noise, mouth, and eyebrow) that are changed with emotion type. The proposed approach is evaluated by standard emotion dataset (JAFFE) in both phases of ANN (training and testing). The simulation results view significant percentage of accuracy in comparison with the existing technique of emotion detection.

[1]  Uttam L. Patel.  Efficiency Improvement in Recognition of Human Facial Expression , 2017 .

[2]  Zhengyou Zhang,et al.  Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[3]  Aditya Bihar Kandali,et al.  Recognition of facial expressions extracting salient features using local binary patterns and histogram of oriented gradients , 2017, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS).

[4]  Bertrand Rivet,et al.  Analyzing Emotional Facial Expressions’ Neural Correlates Using Event-Related Potentials and Eye Fixation-Related Potentials , 2017 .

[5]  Shiqing Zhang,et al.  Facial Expression Recognition via Deep Learning , 2015 .

[6]  Edilson de Aguiar,et al.  Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order , 2017, Pattern Recognit..

[7]  Hillary Anger Elfenbein,et al.  When familiarity breeds accuracy: cultural exposure and facial emotion recognition. , 2003, Journal of personality and social psychology.

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

[9]  Zheru Chi,et al.  Facial Expression Recognition Based on Facial Components Detection and HOG Features , 2014 .

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

[11]  Jian-Jiun Ding,et al.  Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection , 2015, Signal Process..

[12]  Yong Tao,et al.  Compound facial expressions of emotion , 2014, Proceedings of the National Academy of Sciences.