SAttentiveness Measure in Classroom Environment using Face Detection
| Autor: | Rahul kumar pandey, Ayaz Ahmed Faridi, Gyanesh Shrivastava |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Track (disk drive) Measure (physics) Process (computing) 020206 networking & telecommunications 02 engineering and technology Activity monitoring ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Face detection business |
| Zdroj: | 2021 6th International Conference on Inventive Computation Technologies (ICICT). |
| Popis: | The concentration of students in the classroom is very important for an effective learning process. If the students are getting deviated, it must be detected then the teacher should be able to take the necessary steps to avoid the situation. A Multi-tasking Deep Neuro-Fuzzy Model (MDNFM) model is proposed for the accurate prediction of the attentiveness of the students in the classroom. Initially, the images are acquired and transferred to the Capture, Transform and Flow (CTF) tool. Later, these images are preprocessed to make them suitable for face detection (FD) and activity monitoring (AM). This article mainly applies the color models for face detection and proposes a methodology to track the student's attention and produces the output. This system can provide information to the teacher as well as the student. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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