Deep-learning based descriptors in application to aging problem in face recognition

Abstract

Nowadays, “aging” becomes a challenging problem for face recognition systems. The challenge is particularly due to age-related biological changes, which can lead to significant variations in facial characteristics between two images taken at different ages of the same person. As the face is the most heavily part affected by aging, there is a growing need to extracting robust face features for age-invariant face recognition, particularly, in the presence of large age differences of the same person face images. The aim of this paper is to examine effectiveness of deep-learning based methods as features extraction tool for age-invariant face recognition. In this study, we evaluate five popular pre-trained deep-convolutional neural network (CNN) models that are AlexNet, GoogleNet, Inception V3, ResNet50 and SqueezeNet, on a widely used face-aging database, namely FG-NET, using K-nearest neighbors (K-NN), discriminant analysis, and support vector machines (SVM) classifiers. Further, a statistical analysis test is performed to confirm the statistical significance of the obtained results. Experimental results on this database show the promise of using Convolutional Neural Networks(CNN) for face recognition across age progression. Also the AlexNet model appears to be most promising for age-invariant face recognition, as highest mean accuracy rate is always achieved with feature extraction using the AlexNet model. These results are more significant, according to a 95% confidence level hypothesis test.