Deep Representations Based on Sparse Auto-Encoder Networks for Face Spoofing Detection
Automatic face recognition plays significant role in biometrics systems, and face spoofing has raised concerns at the same time, since a photo or video of an authorized uesr’s face could be used for deceiving the system. In this paper, we propose a new hierarchical visual feature based on deep learning to discriminate spoof face. First, the LBP descriptor is used to extract low level face features of face images, and then these low level features are encoded to high level features via a deep learning architecture which is consists of sparse auto-encoder (SAE). Finally, SVM classifier is applied to detect face spoofing. We perform a experimental evaluation on two face liveness detection databases, CASIA database and NUAA database. The results indicate the robustness of the proposed approach for face spoofing detection.
This paper analyzes the use of CBERS (China-Brazil Earth Resources Satellite) high resolution images to map urban limits of a small city in the Amazon Region of Brazil. The use of Sino-Brazilian satellite images to study small cities of the Brazilian Amazon Region is also discussed. A HCR image was used to classify the image of the study area into four classes: urban, forest, clouds, and shadows, by visual interpretation. This map was compared with another map produced with unsupervised classification. A confusion matrix was produced to evaluate the accuracy of unsupervised classification. Comparison between the reference map (visual interpretation) and the unsupervised classification map verified that 82.8% of the areas were classified correctly. This ability to recognize and delimitate small cities in Amazonia demonstrates the quality of the CBERS – HCR image.