Design of face detection and recognition system to monitor students during online examinations using Machine Learning algorithms

Geetha, M.; Latha, R.; Nivetha, S.; Hariprasath, S.; Gowtham, S.; Deepak, C.S.

doi:10.1109/iccci50826.2021.9402553

Cited by 38 publications

(2 citation statements)

References 6 publications

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“…In another study [20], a semantic context-induced attentive fusion network was designed. Eye-tracking [21,22], gaze-tracking [23], and face-tracking algorithms [24] have also been proposed.…”

Section: Related Workmentioning

confidence: 99%

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Napoli¹,

Russo²,

Iocchi³

et al. 2022

OBM Neurobiol

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Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time.

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Section: Related Workmentioning

confidence: 99%

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Napoli¹,

Russo²,

Iocchi³

et al. 2022

OBM Neurobiol

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show abstract

“…When the light source is blocked by objects or other parts of the face, shadows may appear on the face. The low brightness in shadow regions may decrease the quality of the image, reducing the accuracy and effectiveness of some computer vision tasks, such as face biometric identification [ABBR20, XZXH21], facial image editing [DJBY20, JP19], face detection and recognition [GLN*21,AEHM19,WY22], and face modeling [HZLH17,ZLL*20,WLW*20]. Additionally, low‐quality images disrupt the aesthetics of the image and do not satisfy the need for visual appreciation.…”

Section: Introductionmentioning

confidence: 99%

Facial Image Shadow Removal via Graph‐based Feature Fusion

Zhang,

Chen,

Liu

et al. 2023

Computer Graphics Forum

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Despite natural image shadow removal methods have made significant progress, they often perform poorly for facial image due to the unique features of the face. Moreover, most learning‐based methods are designed based on pixel‐level strategies, ignoring the global contextual relationship in the image. In this paper, we propose a graph‐based feature fusion network (GraphFFNet) for facial image shadow removal. We apply a graph‐based convolution encoder (GCEncoder) to extract global contextual relationships between regions in the coarse shadow‐less image produced by an image flipper. Then, we introduce a feature modulation module to fuse the global topological relation onto the image features, enhancing the feature representation of the network. Finally, the fusion decoder integrates all the effective features to reconstruct the image features, producing a satisfactory shadow‐removal result. Experimental results demonstrate the superiority of the proposed GraphFFNet over the state‐of‐the‐art and validate the effectiveness of facial image shadow removal.

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Application of Machine Learning Algorithm Based on Big Data

Wang

2022

Application of Intelligent Systems in Multi-Modal Information Analytics

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Design of face detection and recognition system to monitor students during online examinations using Machine Learning algorithms

Cited by 38 publications

References 6 publications

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Facial Image Shadow Removal via Graph‐based Feature Fusion

Application of Machine Learning Algorithm Based on Big Data

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