Open AccessThe Analysis on Student’ Psychologic Status of Online Learning under Extraction Model from Computer Face Features
Author(s) -
Lan Wen,
Shaogang Yang,
Zhilun Jiao,
Xiaowen Liang,
Yang Xu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012198
Subject(s) - computer science , artificial intelligence , facial recognition system , local binary patterns , face (sociological concept) , adaboost , feature (linguistics) , face detection , realization (probability) , three dimensional face recognition , word error rate , pattern recognition (psychology) , feature extraction , range (aeronautics) , active appearance model , mood , mode (computer interface) , image (mathematics) , psychology , mathematics , support vector machine , statistics , human–computer interaction , histogram , social science , philosophy , psychiatry , linguistics , sociology , materials science , composite material
The research aims to analyze students’ psychological mood during online study and enhance the efficiency of learning. In this research, face features are extracted by LBP (local binary pattern) algorithm based on face feature recognition. The facial feature points are tracked by CLM (constrained local model) algorithm, and the face images are normalized by AdaBoost algorithm. In the end, the fatigue level of learners is calculated by P80 method in PERCLOS criterion to verify the accuracy, recognition rate and error rate of the model. The results indicate that through the recognition of the faces in a complex background, the accuracy of the mode is 95%, the recognition rate is 95.53% and the error rate is only 0.53%, the range of aspect ratio in the human eye image is 0.22 ≤ λopen ≤ 0.27, 0.05 ≤ λclose ≤0.1, and learner’s level of excitement can be estimated according to the range of f. Therefore, this algorithm model can extract learner’s facial features very well and show a good result. Detecting the degree of learner’s excitement accurately can provide an important fundamental for the realization of online learning emotion detection system which also has guiding significance for the development and popularization of the networked education.