TY  - JOUR
AU  - Ben Amer, Hajer Mohamed H 
AU  - Rajamanickam, Dr. Leelavathi 
AU  - Abboud, Dr. Anas A. 
PY  - 2017
TI  - Liveness Detection from Real user, Printed Pictures and Pictures on Mobile Devices from Low Resolution Webcam
JF  - Journal of Computer Science
VL  - 13
IS  - 9
DO  - 10.3844/jcssp.2017.400.407
UR  - https://thescipub.com/abstract/jcssp.2017.400.407
AB  - Biometrics data have emerged as one of the most widely used technologies for validation of identity in various sectors. Nevertheless, spoof biometric data are used by attackers to get access to their targets. Hence, a number of approaches have been initiated to detect these spoofed biometric data. As such, this article proposed a complete methodology for liveness detection using low camera resolution, primarily because vast studies do rely on image quality, eyelid motion and facial expression to investigate spoof images. Nevertheless, spoof attacks cannot be diagnosed from low quality images or recorded video on mobile devices. Therefore, this paper initiates a cutting-edge technique to identify spoof attack from printed pictures, as well as videos recorded on mobile devices and built-in low resolution webcam. Moreover, by detecting the movements at the eye region and weighing these movements from a number of opted frames from recorded video, the standard deviation of these weighted movements were determined and finally, the results of these standard deviation values were compared with the priory estimated threshold values retrieved from this study. Furthermore, due to the nature of the data employed in this study, the researchers generated some data for real users by using low resolution building webcam device by recording the face images of the users on mobile device. With that, 100 various videos were used to predict the threshold value for liveness detection. As a result, this method had been successful in analysing user liveness with an accuracy of 97.6%. On top of that, further experiment is required to look into this method with bigger data set.