Face verification across age progression

Face Recognition is the process of identification of a person by their facial image. This technique makes it possible to use the facial images of a person to authenticate him into a secure system, for criminal identification, for passport verification. Face recognition approaches for still images can be broadly categorized into holistic methods and feature based methods .Holistic methods use the entire raw face image as an input, whereas feature based methods extract local facial features and use their geometric and appearance properties. Face verification in the presence of age progression is an important problem that has not been widely addressed. The problem by designing and evaluating discriminative approaches is discussed. These directly tackle verification tasks without explicit age modeling, which is a hard problem by itself. First, we find that the gradient orientation (GO), after discarding magnitude information, provides a simple but effective representation for this problem. This representation is further improved when hierarchical information is used, which results in the use of the gradient orientation pyramid (GOP). When combined with a support vector machine (SVM) GOP demonstrates excellent performance in this topic, in comparison with seven different approaches including two commercial systems. This topic is conducted on the FGnet dataset and two large passport datasets, one of them being the largest ever reported for recognition tasks. Second, taking advantage of these datasets, we empirically study how age gaps and related issues (including image quality, spectacles, and facial hair) affect recognition algorithms. This topic found surprisingly that the added difficulty of verification produced by age gaps becomes saturated after the gap is larger than four years, for gaps of up to ten years. In addition, we find that image quality and eyewear present more of a challenge than facial hair.