JIS  Vol.8 No.3 , July 2017
Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances
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.
Cite this paper: Khiyari, H. and Wechsler, H. (2017) Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances. Journal of Information Security, 8, 174-185. doi: 10.4236/jis.2017.83012.

[1]   Lanitis, A., Taylor, C.J. and Cootes, T.F. (2002) Toward Automatic Simulation of Aging Effects on Face Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 442-455.

[2]   Ling, H., Soatto, S., Ramanathan, N. and Jacobs, D.W. (2010) Face Verification across Age Progression Using Discriminative Methods. IEEE Transactions on Information Forensics and Security, 5, 82-91.

[3]   Biswas, S., Aggarwal, G., Ramanathan, N. and Chellappa, R. (2008) A Non-Generative Approach for Face Recognition across Aging. 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, 1-6.

[4]   Le Cun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998) Gradient Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86, 2278-2324.

[5]   Krizhevsky, A., Sutskever, I. and Hinton, G. (2012) Imagenet Classification with Deep Convolutional Neural Networks. In NIPS.

[6]   Pan, S.J. and Yang, Q. (2010) A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359.

[7]   Fayin, L. and Wechsler, H. (2005) Open Set Face Recognition. IEEE Transactions on Pattern Analysis and Machine Learning, 27, 1686-1697.

[8]   El Khiyari, H. and Wechsler, H. (2016) Face Recognition across Time Lapse Using Convolutional Neural Networks. Journal of Information Security, 7, 141-151.

[9]   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.

[10]   Parkhi, O.M., Vedaldi, A. and Zisserman, A. (2015) Deep Face Recognition. Proceedings of the British Machine Vision Conference (BMVC).

[11]   Russakovsky, O., Deng, J., Su, H., et al. (2015) ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115, 211-252.

[12]   Takács, B. and Wechsler, H. (1998) Face Recognition Using Binary Image Metrics. Proceedings of the 3rd International Conference on Face & Gesture Recognition, Nara, 14-16 April 1998, 294-299.

[13]   Dubuisson, M. and Jain, A.K. (1994) A Modified Hausdorff Distance for Object Matching. Proceedings of the 12th International Conference on Pattern Recognition (ICPR), Jerusalem, 9-13 October 1994, 566-568.

[14]   Face and Gesture Recognition Working Group (2000).

[15]   Vedaldi, A. and Lenc, K. (2015) MatConvNet: Convolutional Neural Networks for MATLAB. Proceedings of the 23rd ACM international conference on Multimedia, Brisbane, Australia, 26-30 October 2015, 689-692.

[16]   Struc V. and Pavesic, N. (2010) The Complete Gabor-Fisher Classifier for Robust Face Recognition. EURASIP Journal on Advances in Signal Processing, 2010, Article ID: 847680.

[17]   Struc V. and Pavesic, N. (2009) Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition. Informatica (Vilnius), 20, 115-138.

[18]   Klare, B. and Jain, A.K. (2011) Face Recognition across Time Lapse: On Learning Feature Subspaces. IEEE International Joint Conference on Biometrics (IJCB), Washington DC, 11-13 October 2011, 1-8.