{'fraction_non_alphanumeric': 0.041296060991105464, 'fraction_numerical': 0.015247776365946633, 'mean_word_length': 5.274900398406374, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Emotion Tracking System for Students during Online Exams

Many students worldwide endure heavy life events that impact their emotional and mental state, which affect their overall academic performance. The facial expressions recognition system is a great tool to analyze students' performance and behavior during exams period. Unfortunately, there are hardly any studies proposed to develop automatic facial expression recognition systems to trace students and analyze the impact of the emotional state on the student's performance. An Emotional Tracking System (ETS) for students during online exams based on Factor Analysis (FA) and the Very Deep Convolutional Neural Network (VDCNN) classifier is suggested in this study. The proposed ETS consists of three main procedures: preprocessing using the Non-Local Means (N-LM) technique; dimensionality reduction using Factor Analysis (FA) method; and recognition using the VDCNN. Furthermore, the accuracy of the proposed ETS system is evaluated on CK+ face images datasets using VDCNN classifiers. Besides, the accuracy result of the proposed system is compared with the accuracy result produced by a state-of-the-art FERS, which is based on CNN. The experiential results show the effectiveness of the proposed system compared to the FERS. The proposed ETS obtained the best Recognition accuracy (96.05 %, 94.90 %, and 95.96 %) in the training, validation, and testing dataset, respectively, while the state-of-the-art FERS produced (91.96%, 86.73%, and 83.84%) in the training, validation, and testing dataset, respectively.