| Full text | |
| Author(s): |
de Souza, Gustavo B.
;
Santos, Daniel F. S.
;
Pires, Rafael G.
;
Marana, Aparecido N.
;
Papa, Joao P.
;
GarciaGoncalves, LM
;
BeserraGomes, R
Total Authors: 7
|
| Document type: | Journal article |
| Source: | 2017 WORKSHOP OF COMPUTER VISION (WVC); v. N/A, p. 6-pg., 2017-01-01. |
| Abstract | |
Data dimensionality is an important issue to be adressed by pattern recognition systems. Despite of storage and processing, working with high-dimensional feature vectors also requires complex optimization methods. A proper selection of the most important features is essential and dimensionality reduction techniques can also be applied in order to avoid dealing with more information than needed. One of the most important analytical techniques for such task is Principal Component Analysis (PCA). In this work we propose a novel and more robust dimensionality reduction approach based on the Restricted Boltzmann Machines (RBMs), neural networks able to learn the probability distribution of the set of training samples, identifying the best features to discriminate them, for face spoofing detection. Results of the proposed approach show that the features learned and extracted by RBMs are more robust than the ones analytically obtained by PCA for differentiating between real and fake facial images. (AU) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/05522-6 - 50th Annual International Symposium of Circuits and Systems |
| Grantee: | Aparecido Nilceu Marana |
| Support Opportunities: | Research Grants - Meeting - Abroad |