Author(s)

Hachim El Khiyari, Harry Wechsler

Department of Computer Science, George Mason University, Fairfax, VA, USA.

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.

Aging, Biometrics, Convolutional Neural Networks (CNN), Deep Learning, Image Set-Based Face Recognition (ISFR), Transfer Learning

Khiyari, H. and Wechsler, H. (2017) Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances. Journal of Information Security, 8, 174-185. doi: 10.4236/jis.2017.83012.