SECURE ATTENDANCE TRACKING WITH FACIAL RECOGNITION AND LIVENESS DETECTION

  • Asha S, Meer Hyder Ali, Md Afnan Khan, SK Tazim Munaf, Md Ibaduddin

Abstract

Abstract - Biometrics with facial recognition is now widely used. A face identification system should identify not only someone's faces but also detect spoofing attempts with printed face or digital presentations. A sincere spoofing prevention approach is to examine face liveness, such as eye blinking and lips movement. Nevertheless, this approach is helpless when dealing with video-based replay attacks. For this reason, this paper proposes a combined method of face liveness detection and CNN (Convolutional Neural Network) classifier. The anti- spoofing method is designed with two modules, the blinking eye module that evaluates eye openness and lip movement, and the CCN classifier module. The dataset for training our CNN classification can be from a variety of publicly available sources. We combined these two modules sequentially and implemented them into a simple facial recognition application using the Android platform. The test results show that the module created can recognize various kinds of facial spoof attacks, such as using posters, masks, or smartphones. Keywords: face recognition, face spoofing, CNN classifier, face liveness detection, deep learning.

Author Biography

Asha S, Meer Hyder Ali, Md Afnan Khan, SK Tazim Munaf, Md Ibaduddin

Dept. of Computer Science and Engineering, Guru Nanak Dev Engineering College, Bidar, Karnataka, India

How to Cite
Asha S, Meer Hyder Ali, Md Afnan Khan, SK Tazim Munaf, Md Ibaduddin. (1). SECURE ATTENDANCE TRACKING WITH FACIAL RECOGNITION AND LIVENESS DETECTION. International Journal Of Innovation In Engineering Research & Management UGC APPROVED NO. 48708, EFI 5.89, WORLD SCINTIFIC IF 6.33, 11(8), 183-189. Retrieved from http://journal.ijierm.co.in/index.php/ijierm/article/view/2321