An efficient face anti-spoofing and detection model using image quality assessment parameters

Biometric authentication poses a significant problem as reconstructed sample or fake self-manufactured samples used by intruders for accessing the actual real legitimate traits. The other prime concern for biometrics is the increasing demand for safety in mobile devices, such as smartphones and tablets etc. So, in the present scenario security for biometrics has gained considerable attention due to various inherent qualities of biometrics. For detection of valid user in a face recognition system with photographs, videos, and 3D models, face liveness detection system is a great technique against spoofing attacks for differentiating between the fake traits from the real traits. In this paper, a novel fake biometric detection technique utilizing liveness detection is proposed for detecting deceitful access attempts in the biometric face system. The prime objective of the paper is to propose a low-complexity fake biometric detection using different image quality assessment parameters i.e. Mean Square Error, Signal to Noise Ratio,SC etc. on the extracted features of the images. The authenticity of the proposed model is confirmed by analyzing the values of MSE, which are 5.8% and 8.49% more than the threshold value of nose and eye features. The same results have also been shown for other 11 different image quality assessment parameters. The experiments were done on the database prepared using the image samples of the 500 male and female students having age between 20 to 30 years.