Learning a bag of features based nonlinear metric for facial similarity
This article presents a new method aiming at automatically learning a visual similarity between two images from a class model. This kind of problem is present in many research domains such as object tracking, image classification, signing identification, etc. We propose a new method for facial recognition with a system based on non-linear projection and metric learning. To achieve this objective, we feed a “Bag of Features” representation of the face images into a specific neural network that learns a mapping to a more compact and discriminant representation. This learning process aims at non-linearly projecting the facial features into a reduced space where two images belonging to the same category (i.e. a person) are “close” according to a given similarity metric and “distant” otherwise. The proposed method gives very promising results for face identification in adverse conditions like expression, illumination and facial pose variations. Experimental results give 97% correct recognition rate on the CMU PIE database containing 68 individuals, under vary variable pose and illumination conditions.