Shared features for multiple face-based biometrics

People often make instant judgments about the age, health, mood, personality and character of others based on their facial features. It is not clear from a cognitive aspect whether these different traits require different sets of features or a shared feature set. Till date, much of the computational face image analysis work such as face recognition, face-based deceit detection, age estimation, gender estimation, etc, have been developed on datasets and features specific only to the problem-at-hand. In this paper, we explore an approach for performing face image analysis using a shared set of features for different tasks. By performing unsupervised learning on a large collection of face images, we learn the parameters of a probabilistic generative face model, and by projecting a new face image into this probabilistic space, we obtain a set of face features not created for any specific face analysis tasks. We investigate the use of such shared features and successfully predict the level of attractiveness, whether or not a face is made-up, the facial expression, and the gender of a person, given any arbitrary, near-frontal face image.