Abstract
There is a growing incentive to use biometric technology to improve and even replace traditional security methods. Biometric modalities are characteristics drawn from the human body, which are unique to each individual and can be used to establish their identity in a population. Among the biometric modalities, the face is the most commonly seen and used in daily life. Several works have been proposed involving Deep Learning, with emphasis on the Convolutional Neural Networks, for facial recognition. However none of these studies perform a detailed comparative study between traditional machine learning techniques and Deep Learning presenting the pros and cons of each one. In this context, the present work aims to conduct a comparative study between traditional machine learning techniques, such as K-Nearest Neighbors, Optimum-Path Forest, Support Vector Machine, Extreme Learning Machine, Artificial Neural Networks and Deep Learning, focusing on Convolutional Neural Networks, for facial recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Coşkun, M., Uçar, A., Yıldırım, Ö., Demir, Y.: Face recognition based on convolutional neural network. In: 2017 International Conference on Modern Electrical and Energy Systems, MEES, pp. 376–379. IEEE (2017)
Costa, D.M.M.D.: Ensemble baseado em métodos de Kernel para reconhecimento biométrico multimodal. Ph.D. thesis. Universidade de São Paulo (2011)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (2006)
Georghiades, A., Belhumeur, P., Kriegman, D.: Yale face database, vol. 2. Center for computational Vision and Control at Yale University (1997). http://cvc.yale.edu/projects/yalefaces/yalefa
Haykin, S.S., Haykin, S.S., Haykin, S.S., Haykin, S.S.: Neural Networks and Learning Machines, vol. 3. Pearson, Upper Saddle River (2009)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990. IEEE (2004)
Jain, A., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer, Heidelberg (2007). https://doi.org/10.1007/978-0-387-71041-9
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)
Litchfield, J., Wilcoxon, F.: A simplified method of evaluating dose-effect experiments. J. Pharmacol. 96(2), 99–113 (1949)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Martinez, A.M.: The AR face database. CVC technical report 24 (1998)
Müller, K.-R., Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting time series with support vector machines. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0020283
Papa, J.P., Falcao, A.X., Suzuki, C.T.: Supervised pattern classification based on optimum-path forest. Int. J. Imag. Syst. Technol. 19(2), 120–131 (2009)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Yin, Y., Liu, L., Sun, X.: SDUMLA-HMT: a multimodal biometric database. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 260–268. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25449-9_33
Zhang, Z., Li, J., Zhu, R.: Deep neural network for face recognition based on sparse autoencoder. In: 2015 8th International Congress on Image and Signal Processing, CISP, pp. 594–598. IEEE (2015)
Zhao, W.: Research on the deep learning of the small sample data based on transfer learning. In: Proceedings of the AIP Conference, vol. 1864, p. 020018. AIP Publishing (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Finizola, J.S., Targino, J.M., Teodoro, F.G.S., de Moraes Lima, C.A. (2018). A Comparative Study Between Deep Learning and Traditional Machine Learning Techniques for Facial Biometric Recognition. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-03928-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03927-1
Online ISBN: 978-3-030-03928-8
eBook Packages: Computer ScienceComputer Science (R0)