Age-invariant face recognition using multiple descriptors along with modified dimensionality reduction approach

Abstract

Face recognition on the basis of age variation is a significant yet challenging issue. One among the effective methods to age-invariant face recognition is to form a face aging model that can be utilized to recompense for the aging process in face matching or age assessment. The periocular region of a face is the most age-invariant facial region, to abstract discriminative local features that are distinct for every subject. Feature vector space can be reduced by utilizing the features only from the periocular region. So, in this article, features are only extracted from the periocular region of a face. For feature extraction, multiple descriptors such as Scale Invariant Feature Transform (SIFT) and then Speeded Up Robust Features (SURF). As the extracted features vector has high dimensionality, it is decreased to the low dimensionality using the Enhanced Principal Components Analysis (EPCA) method. Finally, these extracted features are given as input to the Artificial Neural Network (ANN) based classifier which performs to recognize the face of the input image. Our projected method is applied and tested by Matlab for FG-NET face aging dataset Simulation results show that the performance of the proposed approach outperforms that of the existing age-invariant face recognition schemes in terms of accuracy, complexity and false recognition ratio.