A real time robust facial expression recognition system using HOG features
This paper presents a facial expression recognition framework which infers the emotional states in real time, thereby enabling the computers to interact more intelligently with people. The proposed method determines the face as well as the facial landmark points, extracts discriminating features from suitable facial regions, and classifies the expressions in real time from live webcam feed. The speed of the system is improved by the appropriate combination of the detection and tracking algorithms. Further, instead of the whole face, histogram of oriented gradients (HOG) features are extracted from the active facial patches which makes the system robust against the scale and pose variations. The feature vectors are further fed to a support vector machine (SVM) classifier to classify into neutral or six universal expressions. Experimental results show an accuracy of 95% with 5 folds cross validation in extended Cohn Kanade (CK+ dataset.
2016 International Conference on Computing, Analytics and Security Trends (CAST)