Emotion Recognition from Facial Action Points by Principal Component Analysis

This paper proposes a novel approach to emotion recognition of a subject employing 36 selected facial action points marked at specific locations on their faces. Facial expressions obtained from the subjects enacting them are recorded, and the corresponding changes in marked action points are evaluated. The measurements reveal that the action points have wider variations in facial expressions containing diverse and intensified emotions. Considering 10 instances for each facial expression, and analysing the same emotion, experimented over 10 subjects, we obtain a set of 100 distance matrices, representing the distance between any two selected action points. The average of 100 matrices for each individual emotion is evaluated, and the first principal component, representing the most prominent features of the average distance matrix is evaluated. In the recognition phase, the first Principal component obtained from the distance matrix of an unknown facial expression is evaluated, and its Euclidean distance with the first Principal component of each emotion is determined. The Euclidean distance between the obtained principal component and that of j-th emotion class is minimum if the obtained emotion accurately falls in the emotion class. Classification of 120 facial images, with equal number of samples for six emotion classes, reveals an average classification accuracy of 92.5%, the highest being recorded in relax and disgust and the least in fear and anger. KeywordsAction points, Emotion Recognition, Principal Component Analysis (PCA).