Computer vision based classroom attendance management system-with speech output using LBPH algorithm

• Published in: International journal of speech technology Vol. 23; no. 4; pp. 779 - 787
• Main Authors: Bhavana, D., Kumar, K. Kishore, Kaushik, N., Lokesh, G., Harish, P., Mounisha, E., Tej, D. Ravi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2020
• Subjects: Artificial Intelligence; Engineering; Signal,Image and Speech Processing; Social Sciences; Face detection method; Bit- byte conversion methods; Image processing; HAAR features; Features matching; Query image; Expressions; Face recognition method; Features extraction; HOG features
• ISSN: 1381-2416; 1572-8110
• DOI: 10.1007/s10772-020-09739-2

Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult to process, especially for a large group of students. Some automated systems developed to overcome these difficulties, have drawbacks like cost, fake attendance, accuracy, intrusiveness. To overcome these drawbacks, there is a need for a smart and automated attendance system. Traditional face recognition systems employ methods to identify a face from the given input but the results are not usually accurate and precise as desired. The system described in this we aim to deviate from such traditional systems and introduce a new approach to identify a student using a face recognition system, the generation of a facial Model. This describes the working of the face recognition system that will be deployed as an Automated Attendance System in a classroom environment. The proposed smart classroom system was tested for a classroom with 20 students at K L University Andhra Pradesh, Vijayawada, India and we got the experimental results to demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. In this paper we selecting of the face recognition and detection giving result using Python language in PYCHARM tool. This requires high end specifications of a system in order to get better results. It won’t run on all the small specification systems. So, this can run only a small database and compare them with the face required.