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Authors
Affiliations
1 Electronics & Telecommunication Engineering Department, Northeastern University, Boston, US
Source
Digital Signal Processing, Vol 12, No 7-9 (2020), Pagination:
Abstract
The smart classroom of the future we envision will greatly enhance the learning experience and achieve seamless communication between students and teachers through real-time detection and machine intelligence. Additionally, facial recognition can capture student emotions such as happiness, sadness, neutrality, anger, nausea, surprise, and more. From this sentiment we analyze it and in the analysis derive the final overall student behavior of a particular speech. So, you can also get results in the form of teacher feedback and student feedback from student behavior. The three main parts of the student attendance system are then described in detail using two deep learning facial recognition algorithms. Behavioral analysis model based on facial recognition neural network or Haar classifier. iii) Automatic teacher feedback based on student behavior analysis.
Keywords
Face Detection, Face Recognition, Face Identification, Behaviour Analysis.
