Active Face Spoof Detection Using Image Distortion Analysis

Authors : Peter ANTHONY, Betül AY

Pages : 435-450

Doi:10.55525/tjst.1142626

View : 18 | Download : 10

Publication Date : 2022-09-30

Article Type : Research Paper

Abstract :With the rising use of facial recognition systems in a range of real-world scenarios and applications, attackers are also increasing their efforts, with a number of spoofing techniques emerging. As a result, developing a reliable spoof detection mechanism is critical. Active-based techniques have been shown to be good at finding spoofs, but they have a number of problems, such as being intrusive, expensive, hard to compute, not being able to be used in many situations, and usually needing extra hardware. This research presented an active-based robust spoof detection technique capable of detecting a wide range of media or 2D attacks while being less intrusive, less expensive, low in complexity, and more generalizable than other active-based techniques. It doesn`t require any additional hardware, so it can easily be integrated into current systems. The distortion variations of video frames of the user`s face collected at varying distances from the camera are analyzed to detect spoofing. Both the legitimate and spoof attack datasets were created using real-world facial photo and video data. The proposed approach achieved a spoof detection accuracy of 98.18% using both machine learning classifiers and a deep learning model, with an equal error rate and a half total error rate as low as 0.023 and 0.021, respectively.
Keywords : Face Anti spoofing, Face Spoof Detection, Face Recognition, Biometrics, Image Distortion Analysis