On the Performance of Classic and Deep Neural Models in Image Recognition
Deep learning has arisen in the last years as a powerful and ultimate tool for machine learning problems. This article analyses the performance of classic and deep neural network models in a challenging problem like face recognition. The aim of this article is to study what the main advantages and disadvantages deep neural networks provide and when they will be more suitable than classic models, which have also obtained really good results in some complex problems. Is it worth using deep learning? The results show that deep models increase the learning capabilities of classic neural networks in problems with high non-linearities features.
KeywordsDeep neural networks Convolutional neural networks Face recognition Object recognition
The authors would like to express his thanks to the project with number PEIC-2014- 003-P and to the authorities that give their support to its development, the FEDER and the Junta de Comunidades de Castilla la Mancha.
- 9.Dantone, M., Bossard, L., Quack, T., Van Gool, L.: Augmented faces, pp. 24–31 (2011)Google Scholar
- 15.Li, Y., Li, W., Wu, G.: An intrusion detection approach using SVM and multiple kernel method. Int. J. Adv. Comput. Technol. 4(1), 463–469 (2012)Google Scholar
- 18.Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks, vol. 1, pp. 586–591 (1993)Google Scholar
- 20.Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.: A 3d facial expression database for facial behavior research, pp. 211–216 (2006)Google Scholar