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Face Recognition across Time Lapse Using Convolutional Neural Networks

Journal of Information Security 2016年第3期7卷 141-151页

作者： hachim el khiyari Harry Wechsler Department of Computer Science George Mason University Fairfax VA USA

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.

关键词： Aging Authentication Biometrics Convolutional Neural Networks (CNN) Deep Learning Ensemble Methods Face Recognition Interoperability Security

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Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances

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Journal of Information Security 2017年第3期8卷 174-185页

作者： hachim el khiyari Harry Wechsler Department of Computer Science George Mason University Fairfax VA USA

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.

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

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