Journal of Information Security, 2016, 7, 141-151 Published Online April 2016 in SciRes. http://www.scirp.org/journal/jis http://dx.doi.org/10.4236/jis.2016.73010 How to cite this paper: El Khiyari, H. and Wechsler, H. (2016) Face Recognition across Time Lapse Using Convolutional Neural Networks. Journal of Information Security, 7, 141-151. http://dx.doi.org/10.4236/jis.2016.73010 Face Recognition across Time Lapse Using Convolutional Neural Networks Hachim El Khiyari, Harry Wechsler Department of Computer Science, George Mason University, Fairfax, VA, USA Received 12 February 2016; accepted 8 April 2016; published 11 April 2016 Copyright © 2016 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. This paper reports the novel use and effectiveness of deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN archi- tecture using the VGG-Face deep (neural network) learning is found to produce highly discrimina- tive and interoperable features that are robust to aging variations even across a mix of biometric datasets. The features extracted show high inter-class and low intra-class variability leading to low generalization errors on aging datasets using ensembles of subspace discriminant classifiers. The classification results for the all-encompassing authentication methods proposed on the chal- lenging FG-NET and MORPH datasets are competitive with state-of-the-art methods including com- mercial face recognition engines and are richer in functionality and interoperability than existing methods as it handles mixed biometric datasets, e.g., FG-NET and MORPH. Keywords Aging, Authentication, Biometrics, Convolutional Neural Networks (CNN), Deep Learning, Ensemble Methods, Face Recognition, Interoperability, Security 1. Introduction Time lapse characteristic of face aging is a complex process that has been studied in various disciplines includ- ing biology, human perception and more recently in biometrics. The effects of aging alter both the shape and texture of the face and vary according to age, time lapse and demographics such as gender and ethnicity. From birth to adulthood, the effects are encountered mostly in the shape of the face, while from adulthood through old age aging affects the face texture (e.g., wrinkles). Face aging is also affected by external factors such as envi-