Blockchain as a Services Based Deep Facial Feature Extraction Architecture for Student Attention Evaluation in Online Education

Blockchain as a Services Based Deep Facial Feature Extraction Architecture for Student Attention Evaluation in Online Education

Obtaining a person’s facial features is necessary for processing techniques like face tracking, facial expression, and face recognition. Many factors are involved in locating and detecting facial features, and the most important is eye localization and detection. Recognition of facial expressions is not about catching expressions; it is about determining whether or not students feel an emotional connection to the material or the instructor who presents it. Using blockchain as a service (BaaS) is the third-party creation and management of cloud-based networks for companies which could use for student attention evaluation without spending time and money developing their in-house solutions. Hence to overcome the problem mentioned, this paper is solved by proposing a new technique named deep facial feature extraction system (DFFE), through which the student’s attention is examined. The basic features such as feelings, interest, and attention of students are evaluated by implementing the new Expert Facial Feature Focus Algorithm (EFFF) using deep learning strategies. It is possible that shortly, this algorithm will discover a person’s feelings and thoughts accurately comprehensively assess user’s attention degrees to help people work, study, and live better with greater efficiency achieving 93.2% by analyzing emotions and feelings.