Widespread deployment of biometric systems has made researchers focus on its vulnerability to even the simplest attempts to breach security through presentation attacks, which involve presenting an artefact (fake sample) to the biometric sensor. We present an approach for presentation attack detection that enables a palm-vein sensor to provide effective countermeasures against these attacks. Our method is based on analysis of noise residual computed from the acquired image. The palm-vein image acquired by the sensor is denoised through median filtering, a well-known nonlinear technique for noise reduction. Subsequently, a noise residual image is obtained by subtracting the denoised image from the acquired image. The local texture features extracted from the noise residual image are then used to detect the presentation attack by means of a trained binary support vector machine classifier. We have performed evaluations on a publicly available palm-vein dataset consisting of 4000 bona fide and fake images collected from 50 subjects in two different sessions. Our approach consistently achieves a perfect average classification error rate of 0.0%. The results also suggest that the proposed approach is more effective than state-of-the-art methods in palm-vein antispoofing.
A number of approaches for personal authentication using palmprint features have been proposed in the literature, majority of which focus on improving the matching performance. However, of late, preventing potential attacks on biometric systems has become a major concern as more and more biometric systems get deployed for wide range of applications. Among various types of attacks, sensor level attack, commonly known as spoof attack, has emerged as the most common attack due to simplicity in its execution. In this paper, we present an approach for detection of display and print based spoof attacks on palmprint verifcation systems. The approach is based on the analysis of acquired hand images for estimating surface re ectance. First and higher order statistical features computed from the distributions of pixel intensities and sub-band wavelet coeefficients form the feature set. A trained binary classifier utilizes the discriminating information to determine if the acquired image is of real hand or a fake one. Experiments are performed on a publicly available hand image dataset, containing 1300 images corresponding to 230 subjects. Experimental results show that the real hand biometrics samples can be substituted by the fake digital or print copies with an alarming spoof acceptance rate as high as 79.8%. Experimental results also show that the proposed spoof detection approach is very effective for discriminating between real and fake palmprint images. The proposed approach consistently achieves over 99% average 10-fold cross validation classification accuracy in our experiments.