Facial Emotion Recognition using DWT based Similarity and Difference features

Recognizing emotions from facial images has become one of the major fields in affective computing arena since it has wide spread applications in robotics, medicine, surveillance, defense, e-learning, gaming, customer services etc. The study used Ekman model with 7 basic emotions- anger, happy, disgust, sad, fear, surprise and neutral acquired from subjects of Indian ethnicity. The acquired data base, Amritaemo consisted of 700 still images of Indian male and female subjects in seven emotions. The images were then cropped manually to obtain the region of analysis i.e. the face and converted to grayscale for further processing. Preprocessing techniques, histogram equalization and median filtering were applied to these after resizing. Discrete Wavelet Transform (DWT) was applied to these pre-processed images. The 2 D Haar wavelet coefficients (WC) were used to obtain the feature parameters. The maximum 2D correlation of mean value of one specific emotion versus all others was considered as the similarity feature. The squared difference of the emotional and neutral images in the transformed domain was considered as the difference feature. Supervised learning methods, K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANN) were used to classify these features separately as well as together. The performance of these parameters were evaluated based on the measures accuracy, sensitivity and specificity.