• Journal of KIISE
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• v.42 no.4
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• pp.483-486
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• 2015

Security systems that use face recognition are vulnerable to spoofing attacks where unauthorized individuals use a photo or video of authorized users. In this work, we propose a method to detect a face spoofing attack with a video of an authorized person. The proposed method uses three sequential frames in the video to extract features by using Fourier Transform and Dense-SIFT filter. Then, classification is completed with a Support Vector Machine (SVM). Experimental results with a database of 200 valid and 200 spoof video clips showed 99% detection accuracy. The proposed method uses simplified features that require fewer memory and computational overhead while showing a high spoofing detection accuracy.