Open AccessDevelopment and Optimization of Check-in System Based on Face Recognition Technology
Author(s) -
Pingping Chen,
Xiaoran Geng,
M. Zha,
Qingming Xu,
Dingying Tan
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/782/5/052022
Subject(s) - adaboost , face detection , computer science , artificial intelligence , android (operating system) , facial recognition system , viola–jones object detection framework , object class detection , mobile phone , computer vision , face (sociological concept) , cheating , pattern recognition (psychology) , support vector machine , operating system , psychology , social psychology , social science , sociology
Face recognition technology is widely used. By comparing the speed and accuracy of Face++ and Baidu AI face recognition, the face sign-in service was collected in the mobile phone or pad APP, which realized the mobile check-in attendance that can be used for college students or training institutions. The system was characterized by accuracy, fast and anti-cheating ability. The experimental results show that Baidu AI’s face detection and recognition performance is better, its speed is more than twice faster than Face++, the recognition score is higher than Face++, and includes live detection, which can effectively prevent static picture cheats. However, Baidu AI’s face detection time (about 271.2ms) was much higher than the commercial face detection algorithm (about 25ms), so the Adaboost face detection algorithm was studied, and the Adaboost algorithm based on OpenCV was transplanted to Android. The platform enables real-time detection of faces. After the system optimization, the face detection time is about 10ms, which effectively improves the face detection speed.