Selection of localised salient features from a single facial image

Abstract

A new application of dynamic link architectures is proposed for the automatic selection of key features in multiple instances of facial images, from manually tagged features in a single facial image of each person. This method is evaluated for its accuracy in face recognition, determined by the frequency with which the model graph fitted to a single instance of an individual's face achieves the closest match to other images of the same person. In addition to the potential of this method for face recognition, the methodology for feature alignment is a practical enabler to recognise emotion in affective computing. The system was first trained to evaluate typical intrapersonal variations of facial features on a training subset with ten facial images from six individuals in the Manchester Face Database (Lanitis et al., 1993). After the training stage, facial features in an image of a new face were assigned the intrapersonal variations obtained for the corresponding features during the training stage. The saliency measure for each local image feature was then computed within a Bayesian framework and the accuracy of face recognition was evaluated with a further three images each from 24 people, taken from the same data set. A refinement of the saliency framework that used only a subset of local features for face recognition further increased the accuracy of face recognition on the same test database.