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 JIS  Vol.8 No.3 , July 2017
Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances
Abstract: 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.
Cite this paper: 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.
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