Securing palm-vein sensors against presentation attacks using image noise residuals

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.