A cloud-based monitoring system via face recognition using Gabor and CS-LBP features

Face detection and recognition is an important topic in security. Currently, ubiquitous monitoring has received a large amount of attention. This paper proposes a cloud-based ubiquitous monitoring system via face recognition. It consists of a monitoring client module for face detection and recognition and a cloud storage module for data visualization. In the monitoring client module, the center-symmetric local Gabor binary pattern feature extraction method is proposed for face recognition, which combines improved multi-scale Gabor and center-symmetric local binary pattern (CS-LBP) features. This method maintains crucial local features, reduces the Gabor filter complexity, and adds rotational invariance and more precise texture information. A large number of experiments on the ORL, Yale-B, and Yale databases show that the proposed method obtains significantly better recognition rates than the LBP, CS-LBP, and Scale Gabor methods. Furthermore, we propose a Web browser-based data visualization that renders the geographic locations of the face detection and recognition results.