Open AccessA Teaching Evaluation System Based On Visual Recognition Technology
Author(s) -
Ying Zhang,
Yige Guo,
Jingrong Hu,
Qiaokang Liang,
Jiang Jin-jian,
Changxing Shao,
Yong Wang
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/782/3/032101
Subject(s) - computer science , attendance , artificial intelligence , task (project management) , key (lock) , facial recognition system , feature (linguistics) , machine learning , service (business) , identification (biology) , object (grammar) , feature extraction , computer security , engineering , linguistics , philosophy , botany , economy , systems engineering , economics , biology , economic growth
This paper designs and implements a student-centered teaching evaluation system based on face recognition and pose estimation technology. Our work firstly combines classroom attendance and behavior analysis in an evaluation system. For checking attendance, we select student faces as the identification object, employing a multi-task cascaded convolutional networks (MTCNN) as a face detector and a deep learning network FaceNet to extract face features. Then the head pose information is analyzed using Ensemble of Regression Trees (ERT) algorithm, which is able to detect 68 key feature points of faces. At last, we design and implement the whole system, including designs of functional modules, service software, database and telecommunication of various parts. This system can check attendance and collect student behavior information automatically, enhancing the intelligent level of the learning and teaching system.