Emotion Detection and Characterization using Facial Features

The human face has peculiar and specific characteristics, therefore it becomes difficult in understanding and identifying the facial expressions. It is easy to identify the facial expression of particular person in any image sequence. If we look to automated recognition system, however, the systems available are quite inadequate and incapable of accurately identify emotions. The area of facial expression identification has many important applications. It is an interactive tool between humans and computers. The user, without using the hand can go-ahead with the facial expressions. Presently, the research on facial expression are on the factors i.e. sad, happy, disgust, surprise, fear and angry. This paper aims to detect faces from any given image, extract facial features (eyes and lips) and classify them into 6 emotions (happy, fear, anger, disgust, neutral, sadness). The training data is passed through a series of filters and processes and is eventually characterized through a Support Vector Machine(SVM), refined using Grid Search. The testing data then tests the data and their labels and gives the accuracy of classification of the testing data in a classification report. Various approaches, including passing the training images through Gabor filter, or transforming images using Histogram of Oriented Gradients(HOG) and Discrete Wavelet Transform(DWT) for better classification of data are implemented. The best result achieved so far is by passing the training images through Histogram of Oriented Gradients(HOG), followed by characterization by SVM, which gives an average precision of 85%.