El Khiyari, H. and Wechsler, H. (2016) Face Verification Subject to Varying (Age, Ethnicity, and Gender) Demographics Using Deep Learning. Journal of Biometrics and Biostatistics, 7, 323.
https://doi.org/10.4172/2155-6180.1000323

has been cited by the following article:

• TITLE: Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances

  AUTHORS: Hachim El Khiyari, Harry Wechsler

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

  JOURNAL NAME: Journal of Information Security, Vol.8 No.3, July 14, 2017

  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.