| dc.description.abstract | In recent years, face recognition and face detection techniques are widely used in various applications, such as access control systems, surveillance system, login system, and community websites etc. However, there are some factors that affect the recognition performance like different lighting conditions, facial expression, face rotation, and occlusion by other objects.
In this paper, we use Multi-scale Block Local Binary Pattern (MB-LBP) to detect face. MB-LBP can overcome different lighting conditions, blurred and noise images. We add multi-angle face images to train the detection classifier, so that classifier can overcome some degree of face rotations. We collect face databases with various lighting conditions, angles, multi-resolution and use multi-layer neural network to train the face recognition system. We compare traditional recognition method with deep convolutional neural network (CNN).
In the experimental analysis, we do test with the videos which we shot, including different lighting conditions and different face angles. In the detection section, according to different parameters, the detection rate can reach up to 91% ~ 97% and about 4 × 10^(-7) false positive rate. We compare the results with different parameters. In the recognition section, comparing with traditional methods, we use 12 classes, including 10 persons, other men, and not men, to train deep convolutional neural network model for face recognition. In the case of 881 test samples, the recognition rate reach to 94.3%. | en_US |