Face/Fingerphoto Spoof Detection under Noisy Conditions by using Deep Convolutional Neural Network
Abstract
Most of the generic camera based biometrics systems, such as face recognition systems, are vulnerable to print/photo attacks. Spoof detection, which is to discriminate between live biometric information and attacks, has received increasing attentions recently. However, almost all the previous studies have not concerned the influence of the image distortion caused by the camera defocus or hand movements during image capturing. In this research, we first investigate local texture based anti-spoofing methods including existing popular methods (but changing some of the parameters) by using publicly available spoofed face/finger photo/video databases. Secondly, we investigate the spoof detection under the camera defocus or hand movements during image capturing. To simulate image distortion caused by camera defocus or hand movements, we create blurred test images by applying image filters (Gaussian blur or motion blur filters) to the test datasets. Our experimental results demonstrate that modifications of the existing methods (LBP, LPQ, DCNN) or the parameter tuning can achieve less than 1/10 of HTER(half total error rate)compared to the existing results. Among the investigated methods, the DCNN (AlexNet) can achieve the stable accuracy under the increasing intensity of the blurring noises.