Face recognition across age progression is remains one of the area's most challenging tasks now a days, as the aging process affects both the shape and texture of a face. One possible solution is to apply a probabilistic model to represent a face simultaneously with its identity variable, which is stable through time, and its aging variable, which changes with time. This paper proposes a deep learning and set-based approach to the 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 the sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of the deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for the 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.
CNN- Convolution Neural network, AIFR-Age invariant face recognition, Polling, Flattening, Keras
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