face verification with aging using adaboost and local binary patterns
In this paper, we study the face verification task across age by constructing a simple but powerful representation of the face which uses Local Binary Pattern (LBP) histograms. The spatial information is incorporated by constructing a hierarchical representation of the face image and computing the LBP histogram at each level. A set of most discriminative LBP features of the face are extracted using the AdaBoost learning algorithm. A strong classifier is built using a set of weak classifiers extracted and is used for classification purposes. Several experiments on the FGnet and the MORPH database were performed and the results indicate a significant improvement in the performance when compared with other discriminative approaches. Performance improvement is achieved with smaller age gaps between image pairs and it stabilizes as the age gap increases. Also, the facial hair, glasses, etc. provide discriminative cues to the system in face verification.
face verification aging adaboost local binary patterns