Modelling Uncertainty in Representation of Facial Features for Face Recognition

Face is a more common and important biometric identifier for recognizing a person in a non-invasive way. The face recognition involves identification of the facial features, namely, eyes, eyebrows, nose, mouth, ears, and their spatial interrelationships uniquely. The variability in the facial features of the same human face due to changes in facial expressions, illumination and poses shall not alter the face recognition. In the present chapter we have discussed the modeling of the uncertainty in information about facial features for face recognition under varying face expressions, poses and illuminations. There are two approaches, namely, fuzzy face model based on fuzzy geometric rules and symbolic face model based on extension of symbolic data analysis to PCA and its variants. The effectiveness of these approaches is demonstrated by the results of extensive experimentation using various face databases, namely, ORL, FERET, MIT-CMU and CIT. The fuzzy face model as well as symbolic face model are found to capture variability of facial features adequately for successful face detection and recognition.