TITLE:
Face Recognition across Time Lapse Using Convolutional Neural Networks
AUTHORS:
Hachim El Khiyari, Harry Wechsler
KEYWORDS:
Aging, Authentication, Biometrics, Convolutional Neural Networks (CNN), Deep Learning, Ensemble Methods, Face Recognition, Interoperability, Security
JOURNAL NAME:
Journal of Information Security,
Vol.7 No.3,
April
11,
2016
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 architecture using the VGG-Face deep (neural network) learning is found
to produce highly discriminative 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 challenging FG-NET and MORPH datasets are competitive
with state-of-the-art methods including commercial 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.