Dynamic Model of Facial Expression Recognition based on Eigen-face Approach
This addresses the problem of enabling natural human-computer interaction through emotion recognition, but it is incremental as it builds on existing eigen-face and PCA methods with a person-specific adaptation.
The paper tackles facial expression recognition by proposing a holistic approach that captures temporal variations in facial features and classifies image sequences into emotions, achieving classification of four basic emotions (happiness, sadness, surprise, anger) using gray-scale frontal face images.
Emotions are best way of communicating information; and sometimes it carry more information than words. Recently, there has been a huge interest in automatic recognition of human emotion because of its wide spread application in security, surveillance, marketing, advertisement, and human-computer interaction. To communicate with a computer in a natural way, it will be desirable to use more natural modes of human communication based on voice, gestures and facial expressions. In this paper, a holistic approach for facial expression recognition is proposed which captures the variation in facial features in temporal domain and classifies the sequence of images in different emotions. The proposed method uses Haar-like features to detect face in an image. The dimensionality of the eigenspace is reduced using Principal Component Analysis (PCA). By projecting the subsequent face images into principal eigen directions, the variation pattern of the obtained weight vector is modeled to classify it into different emotions. Owing to the variations of expressions for different people and its intensity, a person specific method for emotion recognition is followed. Using the gray scale images of the frontal face, the system is able to classify four basic emotions such as happiness, sadness, surprise, and anger.