%0 Journal Article
%T Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances
%A Hachim El Khiyari
%A Harry Wechsler
%J Journal of Information Security
%P 174-185
%@ 2153-1242
%D 2017
%I Scientific Research Publishing
%R 10.4236/jis.2017.83012
%X Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for both face identification and face verification. We also find that by using set-based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones.
%K Aging
%K Biometrics
%K Convolutional Neural Networks (CNN)
%K Deep Learning
%K Image Set-Based Face Recognition (ISFR)
%K Transfer Learning
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=77647