Combined Approach to Classify Human Emotions Based on the Hand Gesture

The face and hand is the main part of the human body for interaction during communication. Human emotions through facial and hand gesture is the result of the muscle movement of eyes, nose, head, lips, and hand gesture. The importance of this recognition system is beneficial to many different real-time fields such as psychology, neurology, education, and medical science for the patients specifically as well as the day-to-day life like driver monitoring systems, social interactions, cognitive science, automated tutoring systems, or smart environments which are mainly part of the Human–Computer Interaction (HCI). The aim of this work is to analyze the recent advances in image processing and machine learning techniques with respect to facial emotion recognition based on hand position surrounding the face through the hand gesture. Here, PCA is used for feature extraction and for the normalization Dynamic Time Warping (DTW) is used. Viola–Jones Methods are used to identify prominent features of the face and hand; Gabor filters are used to represent facial geometry at selected locations of fiducial points and combination of multi-class AdaBoost with feature vector and the combination of Hidden Markov Model (HMM) and Support Vector Machines (SVM) is used for the classification of the human emotions based on hand gesture.