We present a residual learning-based convolutional neural network, referred to as DeepResPore, for detection of pores in high-resolution fingerprint images. Specifically, the proposed DeepResPore model generates a pore intensity map from the input fingerprint image. Subsequently, the local maxima filter is operated on the pore intensity map to identify the pore coordinates. The results of our experiments indicate that the proposed approach is effective in extracting pores with a true detection rate of 94.49% on test set I and 93.78% on test set II of the publicly available PolyU HRF dataset at a false detection rate of 8.5%. Most importantly, the proposed approach achieves state-of-the-art performance on both test sets.
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.
Automated human authentication using features extracted from palmprint images has been studied extensively in the literature. Primary focus of the studies thus far has been the improvement of matching performance. As more biometric systems get deployed for wide range of applications, the threat of impostor attacks on these systems is on the rise. The most common among various types of attacks is the sensor level spoof attack using fake hands created using different materials. This paper investigates an approach for securing palmprint based biometric systems against spoof attacks that use photographs of the human hand for circumventing the system. The approach is based on the analysis of local texture patterns of acquired palmprint images for extracting discriminatory features. A trained binary classifier utilizes the discriminating information to determine if the input image is of real hand or a fake one. Experimental results, using 611 palmprint images corresponding to 100 subjects in the publicly available IITD palmprint image database, show that 1) palmprint authentication systems are highly vulnerable to spoof attacks and 2) the proposed spoof detection approach is effective for discriminating between real and fake image samples. In particular, the proposed approach achieves the best classification accuracy of 97.35%.
This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palmprint features.
Here, we aim to improve the accuracy and robustness of existing palmprint authentication systems using 3D palmprint
features. The proposed system uses an active stereo technique, structured light, to capture 3D image or range data of the
palm and a registered intensity image simultaneously. The surface curvature based method is employed to extract
features from 3D palmprint and Gabor feature based competitive coding scheme is used for 2D representation. We
individually analyze these representations and attempt to combine them with score level fusion technique. Our
experiments on a database of 108 subjects achieve significant improvement in performance (Equal Error Rate) with the
integration of 3D features as compared to the case when 2D palmprint features alone are employed.