Abstract
With the advent of ubiquitous facial recognition technology in our everyday life, face spoofing presents a serious threat to the reliability of the security of the system. A spoofing attack occurs when a person tries to impersonate another person's biometric traits in order to circumvent the biometric security of the system. We have seen a lot of work being done to create systems, both intrusive and nonintrusive, to tackle the ingenious ways in which spoofing attacks try to bypass the biometric authorization systems but at the cost of computation or robustness. In this paper, we propose a robust, computationally swift and non-intrusive method to detect face spoofing attacks consisting of recaptured photographs of faces using Local Binary Patterns(LBP) and Specular Reflection. We consider the application as a binary classification problem and make use of Support Vector Machine(SVM) classifier to classify the photograph into real or fake. Experimental analysis shows competitive results of our method on publicly available datasets when compared to other works.
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URL
http://arxiv.org/abs/1905.06540