Detection of Subject Attention in an Active Environment Through Facial Expressions Using Deep Learning Techniques and Computer Vision
| Autor: | Ahmed Husnain Johar, Adnan ul Hasan, Umer Asgher, Talha Yousuf, Imran Malik, Naqash Gerard, Faisal Shafait |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Advances in Neuroergonomics and Cognitive Engineering ISBN: 9783030510404 AHFE (1) |
| DOI: | 10.1007/978-3-030-51041-1_43 |
| Popis: | This research aims for investigation of workers in an industrial environment and can be used as an alternate for monitoring an attention of operator in real-time. Detection of attentiveness and non-attentiveness of people working in an industry could help to identify the weaknesses and strengths of any industrial organization. Human factor is the main and the most critical part of any industrial organization. As a special case, we have established how to detect student attention in the classroom using deep learning techniques along with computer vision. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are used to extract the percentage of attentiveness and non-attentiveness of students based on the student emotions in the classroom. We used the FER-2013 data set for this paper. As per the study, human has finite number of emotions. So, it is easy if we include some emotions in an attentive (Happy, Anger, Surprised and Neutral) domain and some emotions in non-attentive (Sad, Fear and Disgust) domain. This will help the teacher in a way that he can easily evaluate his class attentiveness. On another side, it is also the evaluation of the teacher’s teaching methodology because if the students are engaged in his lecture it means his teaching methodology is good and if most of the students are not engaged then the teacher needs to revise his methodology of teaching in order to engage his class during the lecture. |
| Databáze: | OpenAIRE |
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