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In recent years, artificial intelligence has proved its potential in many fields, especially in computer vision. Facial recognition is one of the most essential tasks in the field of computer vision with various prospective applications from academic research to intelligence service. In this paper, we propose an efficient deep learning approach to facial recognition. Our approach utilizes the architecture of ArcFace model based on the backbone MobileNet V2, in deep convolutional neural network (DCNN). Assistive techniques to increase highly distinguishing features in facial recognition. With the supports of the facial authentication combines with hand gestures recognition, users will be able to monitor and control his home through his mobile phone/tablet/PC. Moreover, they communicate with data and connect to smart devices easily through IoT technology. The overall proposed model is 97% of accuracy and a processing speed of 25 FPS. The interface of the smart home demonstrates the successful functions of real-time operations.
Thai-Viet Dang (Sun,) studied this question.