Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach

Saba, Tanzila; Jamail, Nor Shahida Mohd; Marie-Sainte, Souad Larabi; Raza, Mudassar; Sharif, Muhammad

doi:10.1109/access.2021.3068223

Cited by 30 publications

(15 citation statements)

References 47 publications

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“…Saba et al ( 2021 ), developed an automatic exam activity recognition system, which monitors the body movements of the students through surveillance cameras and classifies activities into six categories using a deep learning approach. The action categories are normal performing, looking back, watching towards the front, passing gestures to other fellows, watching towards left or right, and other suspicious actions.…”

Section: Resultsmentioning

confidence: 99%

“… 15 (Topîrceanu, 2017 ) Breaking up Friendships in Exams: a Case Study for Minimizing Student Cheating in Higher Education Using Social Network Analysis Journal - ✓ - - 16 5 Methods are discussed for identifying students’ friends via their social network analysis, to divide friends into different groups. 16 (Saba et al, 2021 ) Categorizing the Students' Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach Journal - - ✓ - 0 5 Designed an automated exam proctor that categorizes students’ body movements. 17 (Kasliwal, 2015 ) Cheating Detection in Online Examinations Thesis - ✓ ✓ ✓ 2 5 Developed and analyzed a tool for monitoring students’ browsing activities.…”

Section: Appendixmentioning

confidence: 99%

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A systematic review of research on cheating in online exams from 2010 to 2021

2022

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Section: Resultsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

A systematic review of research on cheating in online exams from 2010 to 2021

2022

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“…Now cameras are monitoring every minor detail. [9][10][11] There are different perspectives of engagement, including technology, education and interaction. In this article, we have targeted examinee invigilation in an educational environment.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, how much time the teacher utilized in teaching and how much time the teacher wasted. Now cameras are monitoring every minor detail 9–11 …”

Section: Introductionmentioning

confidence: 99%

Classification of human's activities from gesture recognition in live videos using deep learning

Rehman

Saba

Khan

et al. 2022

Concurrency and Computation

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The automated behavior analysis is a significant problem in the examination. In the presence of invigilators and examiners, examinees use various unfair means to cheat the staff. Examinees may use different gestures like moving their head, eyes and using different suspicious objects (mobile phone, calculator) and so forth. This research is intended to automatically identify and distinguish examinees in live videos who cheat by their activities in examination hall during exams. Hence, we took the privilege of ensemble learning using deep learning-based algorithms. The suspicious head movements and prohibited objects have been monitored using fine-tuned Faster RCNN algorithm. At the same time, interactive use-age of prohibited objects find out using Open-Pose architecture. For this purpose, intersection over union (IOU) has been calculated between the region of interest (ROI) of detected objects and pose points of hands and face. To get the identity of a specific examinee, we stored the features of facial ROI after the last convolution layer and matched them at the time of testing for statistical analysis. We achieved state-of-the-art results using standard evaluating measures. Moreover, for experiments, dataset of automatic examinee invigilation is available currently. So, this research also contributes toward the generation and annotation of the dataset.

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“…In the current era, technology is growing at a rapid pace, and there can be seen exponential growth in the field of identity recognition in image processing (Ross, L., & Russ, J. C., 2011). Many areas like surveillance (M. Simonelli and A. Quaglio, 2015), Human activities recognition (Saba, T., Rehman, A., Jamail, N. S. M., et al, 2021), telecommunication, HCI (human-computer interaction) and image processing, etc., are covering most of the technical areas. We can see the economic significance of the field by seeing around the commercial as well as law enforcement applications worldwide.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Gender Recognition With Handcrafted Feature Ensembles

Ahad¹,

Fayyaz²

2021

AzJHPC

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Human gender recognition is one the most challenging task in computer vision, especially in pedestrians, due to so much variation in human poses, video acquisition, illumination, occlusion, and human clothes, etc. In this article, we have considered gender recognition which is very important to be considered in video surveillance. To make the system automated to recognize the gender, we have provided a novel technique based on the extraction of features through different methodologies. Our technique consists of 4 steps a) preprocessing, b) feature extraction, c) feature fusion, d) classification. The exciting area is separated in the first step, which is the full body from the images. After that, images are divided into two halves on the ratio of 2:3 to acquire sets of upper body and lower body. In the second step, three handcrafted feature extractors, HOG, Gabor, and granulometry, extract the feature vectors using different score values. These feature vectors are fused to create one strong feature vector on which results are evaluated. Experiments are performed on full-body datasets to make the best configuration of features. The features are extracted through different feature extractors in different numbers to generate their feature vectors. Those features are fused to create a strong feature vector. This feature vector is then utilized for classification. For classification, SVM and KNN classifiers are used. Results are evaluated on five performance measures: Accuracy, Precision, Sensitivity, Specificity, and Area under the curve. The best results that have been acquired are on the upper body, which is 88.7% accuracy and 0.96 AUC. The results are compared with the existing methodologies, and hence it is concluded that the proposed method has significantly achieved higher results.

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Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach

Cited by 30 publications

References 47 publications

A systematic review of research on cheating in online exams from 2010 to 2021

A systematic review of research on cheating in online exams from 2010 to 2021

Classification of human's activities from gesture recognition in live videos using deep learning

Pedestrian Gender Recognition With Handcrafted Feature Ensembles

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