Evaluation of Fine Tuning and Feature Extraction methods in Biometric Periocular Recognition

• William Barcellos Universidade de São Paulo
• Nicolas Hiroaki Shitara Universidade de São Paulo
• Carolina Toledo Ferraz Unifaccamp
• Raissa Tavares Vieira Queiroga Universidade Federal do Rio Grande do Norte
• Jose Hiroki Saito Unifaccamp
• Adilson Gonzaga Universidade de São Paulo

DOI: https://doi.org/10.5753/wvc.2019.7626

Resumo

The aim of this paper is to evaluate the performance of Transfer Learning techniques applied in Convolucional Neural Networks for biometric periocular classification. Two aspects of Transfer Learning were evaluated: the technique known as Fine Tuning and the technique known as Feature Extraction. Two CNN architectures were evaluated, the AlexNet and the VGG-16, and two image databases were used. These two databases have different characteristics regarding the method of acquisition, the amount of classes, the class balancing, and the number of elements in each class. Three experiments were conducted to evaluate the performance of the CNNs. In the first experiment we measured the Feature Extraction accuracy, and in the second one we evaluated the Fine Tuning performance. In the third experiment, we used the AlexNet for Fine Tuning in one database, and then, the FC7 layer of this trained CNN was used for Feature Extraction in the other database. We concluded that the data quality (the presence or not of class samples in the training set), class imbalance (different number of elements in each class) and the selection method of the training and testing, directly influence the CNN accuracy. The Feature Extraction method, by being more simple and does not require network training, has lower accuracy than Fine Tuning. Furthermore, Fine Tuning a CNN with periocular's images from one database, doesn't increase the accuracy of this CNN in Feature Extraction mode for another periocular's database. The accuracy is quite similar to that obtained by the original pre-trained network

Palavras-chave: CNN, transfer learning, fine tuning, feature extraction biometric periocular recognition

Referências

Park, U.; Ross, A. ; Jain, A. ; Periocular biometrics in the visible spectrum: a feasibility study. In: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, BTAS ' 09, pp. 1 - 6.

Park, U.; Jillela, R. R.;, Ross, A. ; Jain, A. K. ; Periocular biometrics in the visible spectrum. IEEE Trans on Information Forensics Security, vol. 6, no. 1, march 2011, pp. 96 - 106. DOI: 10.1109/tifs.2010.2096810

Ambika, D. ; Radhika, K. ; Seshachalam, D. ; The eye says it all: periocular region methodologies, in: International Conference on Multimedia Computing and Systems (ICMCS), 2012, pp. 180 - 185. DOI: 10.1109/icmcs.2012.6320240

Li, Z. ; Hoiem, D. ; Learning Without Forgetting, B. Leibe et al. (Eds.): ECCV 2016, Part IV, LNCS 9908, Springer International Publishing AG 2016, pp. 614 - 629, DOI: 10.1007/978-3- 319-46493-0 37

Woodard, D .L., Pundlik, S. J., Lyle, J. R., Miller, P. E. ; Periocular region appearance cues for biometric identification. Proc IEEE Computer Vision and Pattern Recognition Biometrics Workshop, 2010, CVPRW.

Tan, C.W., Kumar, A. ; Human identification from at-a-distance images by simultaneously exploiting iris and periocular features. Proc Intl Conf Pattern Recognition, ICPR, 2012, pp. 553 - 556. DOI: 10.1109/icb.2012.6199824

Karahan, S., Karaoz, A., Ozdemir, O., Gu, A. ; Uludag, U. ; On identification from periocular region utilizing SIFT and SURF. Proc European Signal Processing Conf, EUSIPCO, 2014, pp. 1392 - 1396.

Alonso-Fernandez, F. ; Bigun, J.; A survey on periocular biometrics research, Pattern Recognit. Lett., vol. 82, pp. 92 - 105, Oct. DOI: 10.1016/j.patrec.2015.08.026

Rattani, A ; Derakhshani, R. ; Ocular biometrics in the visible spectrum: A survey, Image and Vision Computing, Elsevier, Volume 59, March 2017, Pages 1- 16. DOI: 10.1016/j.imavis.2016.11.019

Proença, H.; Neves, J.C. ; Deep-PRWIS : Periocular Recognition Without the Iris and Sclera Using Deep Learning Frameworks, IEEE Transactions On Information Forensics And Security, Vol. 13, No. 4, April 2018, pp. 888 - 896. DOI: 10.1109/tifs.2017.2771230

Zhao, Z. ; Kumar, A. ; Accurate periocular recognition under less constrained environment using semantics-assisted convolutional neural network, IEEE Trans. Inf. Forensics Security, vol. 12, no. 5, pp. 1017 - 1030, May 2016, doi: 10.1109/TIFS. 2636093.

Zhao, Z. ; Kumar, A. ; Improving Periocular Recognition by Explicit Attention to Critical Regions in Deep Neural Network, IEEE Transactions On Information Forensics And Security, Vol. 13, N 12, December 2018, pp. 2937 - 2952. DOI: 10.1109/tifs.2018.2833018

Lecun, Y. ; Bottou, L. ; Bengio, Y. ; Haffner, P. ; Gradient-based learning applied to document recognition, Proceedings of the IEEE, Vol: 86, Issue: 11, November 1988, pp. 2278 - 2324. DOI: 10.1109/5.726791

Krizhevsky, A. ; Sutskever, I. ; Hinton, G. E.; ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems. DOI: 10.1145/3065386

Simonyan, K. ; Zisserman, A. ; Very Deep Convolutional Networks For Large-Scale Image Recognition, ICLR 2015, arXiv: 1556 [cs.CV]

Girshick, R.; Donahue, J. ; Darrell, T. ; Malik, J.; Rich feature hierarchies for accurateobject detection and semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June DOI: 10.1109/cvpr.2014.81

Donahue, J.; Jia, Y. ; Vinyals, O., Hoffman, J. ; Zhang, N.; Tzeng, E. ; Darrell, T. ; DeCAF: a deep convolutional activation feature for generic visual recognition. In: Int. Conf. in Machine Learning (ICML) ( 2014).

http://imagem.sel.eesc.usp.br/base/iris/Gallery/index-2.html.

Phillips, P.J.; Bowyer, K.W. ; Flynn, P.J. ; Liu, X. ; Scruggs, W.T.; The Iris Challenge Evaluation 2005, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems, 29 Sept.-1 Oct. DOI: 10.1109/btas.2008.4699333