This paper is published in Volume-7, Issue-2, 2021
Area
Computer Science and Engineering
Author
S. Boobathi Raj, K. Tamilselvi, S. Adharsh, S. Mohamed Javith
Org/Univ
P. A. College of Engineering and Technology, Coimbatore, Tamil Nadu, India
Pub. Date
17 March, 2021
Paper ID
V7I2-1204
Publisher
Keywords
Liveness detection, Face recognition, Haar Cascade Classifier, LBPH, Keras, CNN, Automated attendance.

Citationsacebook

IEEE
S. Boobathi Raj, K. Tamilselvi, S. Adharsh, S. Mohamed Javith. Face Identification and Liveness Detection using CNN for Automated Attendance System, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
S. Boobathi Raj, K. Tamilselvi, S. Adharsh, S. Mohamed Javith (2021). Face Identification and Liveness Detection using CNN for Automated Attendance System. International Journal of Advance Research, Ideas and Innovations in Technology, 7(2) www.IJARIIT.com.

MLA
S. Boobathi Raj, K. Tamilselvi, S. Adharsh, S. Mohamed Javith. "Face Identification and Liveness Detection using CNN for Automated Attendance System." International Journal of Advance Research, Ideas and Innovations in Technology 7.2 (2021). www.IJARIIT.com.

Abstract

A real-time automated attendance system is designed using the method of live face detection and recognition. The system supports multi-user attendance and face liveness detection at the same time. The system can automatically collect face data, that will be saved in the specified dataset folder of each individual person obtained during the registration process. The face detection part of the system is based on Haar Cascade Classifier, and the face recognition part is based on the Local Binary Pattern Histogram algorithm. The algorithm implementation is based on Keras and TensorFlow framework, and the face liveness detection part is based on CNN that creates a 3D model of face detected to differentiate between real and fake images. The attendance system is written in Python language, and the user interface is designed by pywebview library. The experimental results show that the system achieves a good performance in real-time face recognition.
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