| dc.description.abstract | Through the promotion of smart cities and smart families, people are paying more attention to the quality of life. They look forward to change the style of life by technology. The age of big data and GPU acceleration improvement brought deep learning a revolutionary progress in various fields, especially in computer vision. Technology derives from humanity, and technology makes our lives more convinent. There are various forms of access control system, such as keys, access cards, biometric identification, around us. Biometric identification distinguish different people by the unique characteristics of each person. Therefore users don’t need to bring any forms of keys anymore. Faces identification are the most convenient method for all biometric identification, because they don′t need to touch anything or make any extra moves. However, it’s the most complex method.
We propose an access control system that performs face detection and face recognition dynamically, which makes users no need to stop for recognition. We use SSD(Single Shot MultiBox Detector)as the main model for face detection and VGGFace as the main model for face recognition. Through the collection of dataset, data augmentation, pre-processing of image, post-processing of image, and experimental design, we train the robust face detection and face recognition sybsystems. The system use continuous images as input to determine whether it’s a laboratory member. Using continuous frame as input can avoid false positive case make the system output wrong result. We use 1280x960 color video for experimental testing, and achieve about 30fps speed under GPU acceleration.
| en_US |