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• W4313444351 abstract "During the pandemic, online classes are predominated. However, the new normal needs effective analysis of students’ classroom engagement. Offline classes also have a potential threat to students’ engagement before and especially after the post-covid. Facial Expressions Analysis has become essential in the learning environment, whether it is online or offline. The offline classroom environment is considered a problem environment. Since, it can be easily adapted to the online environment. Notably, in the PTZ camera environment, the recognition becomes more challenging due to varying face poses, limited Field-of-View (FOV), illumination conditions, effects of the continuous pan, zoom-in, and zoom-out. In this paper, facial expression-based student engagement analysis in a classroom environment is proposed. Face detection has been achieved by YOLO (You only look once) detector to find multiple faces in the classroom with maximum speed and accuracy. Consequently, by adopting the Ensemble of Robust Constrained Local Models (ERCLM) method, landmark points are localized in detected faces even in occlusion, and therefore, feature matching is performed. Besides, the matched landmark points are aligned by an affine transformation. Finally, having different expressions, the aligned faces are fed as input to Faster R-CNN (Faster Regions with Convolutional Neural Network). It recognizes behavioral activities such as Attentiveness (Zero-In (ZI)), Non-Attentiveness (NA), Day Dreaming (DD), Napping (N), Playing with Personal Stuff in Private (PPSP), and Talking to the Students’ Behind (TSB). The proposed approach is demonstrated using the TCE classroom datasets and Online datasets. The proposed framework outperforms the state-of-the-art algorithms." @default.
• W4313444351 created "2023-01-06" @default.
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• W4313444351 date "2023-01-01" @default.
• W4313444351 modified "2023-09-28" @default.
• W4313444351 title "PTZ-Camera-Based Facial Expression Analysis using Faster R-CNN for Student Engagement Recognition" @default.
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• W4313444351 doi "https://doi.org/10.1007/978-981-19-7169-3_1" @default.
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