Diego Gragnaniello

An Investigation of Local Descriptors for Biometric Spoofing Detection

Biometric authentication systems are quite vulnerable to sophisticated spoofing attacks. To keep a good level of security, reliable spoofing detection tools are necessary, preferably implemented as software modules. The research in this field is very active, with local descriptors, based on the analysis of microtextural features, gaining more and more popularity, because of their excellent performance and flexibility. This paper aims at assessing the potential of these descriptors for the liveness detection task in authentication systems based on various biometric traits: fingerprint, iris, and face. Besides compact descriptors based on the independent quantization of features, already considered for some liveness detection tasks, we will study promising descriptors based on the joint quantization of rich local features. The experimental analysis, conducted on publicly available data sets and in fully reproducible modality, confirms the potential of these tools for biometric applications, and points out possible lines of development toward further improvements.

Fingerprint liveness detection based on Weber Local image Descriptor

In this paper, we investigate the use of a local discriminative feature space for fingerprint liveness detection. In particular, we rely on the Weber Local Descriptor (WLD), which is a powerful and robust descriptor recently proposed for texture classification. Inspired by Webers law, it consists of two components, differential excitation and orientation, evaluated for each pixel of the image. Joint histograms of these components are then processed to build the discriminative features used to train a linear kernel SVM classifier. Experimental results with different databases and different sensors show WLD to perform favorably compared to the state-of-the-art methods in fingerprint liveness detection. In addition, by combining WLD with LPQ (Local Phase Quantization) results further improve significantly.

Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques

We propose an image forgery localization technique which fuses the outputs of three complementary tools, based on sensor noise, machine-learning and block-matching, respectively. To apply the sensor noise tool, a preliminary camera identification phase was required, followed by estimation of the camera fingerprint, and then forgery detection and localization. The machine-learning is based on a suitable local descriptor, while block-matching relies on the PatchMatch algorithm. A decision fusion strategy is then implemented, based on suitable reliability indexes associated with the binary masks. The proposed technique ranked first in phase 2 of the first Image Forensics Challenge organized in 2013 by the IEEE Information Forensics and Security Technical Committee (IFS-TC).

Image forgery detection through residual-based local descriptors and block-matching

We propose a new image forgery detection technique which fuses the outputs of two very diverse tools, based on machine learning and block-matching, respectively. The machine-learning tool builds upon some local descriptors recently proposed in the steganalysis field, which are selected and merged based on an ad hoc measure of reliability. The block-matching tool leverages on the patchmatch algorithm for fast search of candidate matchings. Both tools are fine-tuned so as to optimize their fusion which, in turn, exploits the respective strengths and weaknesses of each tool. The proposed technique ranked first in phase 1 of the first Image Forensics Challenge organized in 2013 by the IEEE Signal Processing Society.

Iris liveness detection for mobile devices based on local descriptors

We propose to use local binary patterns (LBP) on image residual for iris liveness detection.Low-complexity interpolation-free implementation enables use on mobile devices.Performance is promising for both print-based and screen-based attacks. Iris recognition is well suited to authentication on mobile devices, due to its intrinsic security and non-intrusiveness. However, authentication systems can be easily tricked by attacks based on high-quality printing. A liveness detection module is therefore necessary. Here, we propose a fast and accurate technique to detect printed-iris attacks based on the local binary pattern (LBP) descriptor. In order to improve the discrimination ability of LBP and better explore the image statistics, LBP is performed on a high-pass version of the image with 3 i? 3 integer kernel. In addition a simplified interpolation-free descriptor is considered and finally a linear SVM classification scheme is used. The detection performance, measured on standard databases, is extremely promising, despite the resulting very low complexity, which makes possible the implementation for the relatively small CPU processing power of a mobile device.

The 1st Competition on Counter Measures to Finger Vein Spoofing Attacks

The vulnerability of finger vein recognition to spoofing attacks has emerged as a crucial security problem in the recent years mainly due to the high security applications where biometric technology is used. Recent works shown that finger vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate counter-measures against this type of fraudulent actions. The goal of the 1st Competition on Counter Measures to Finger Vein Spoofing Attacks is to challenge researchers to create counter-measures that can detect printed attacks effectively. The submitted approaches are evaluated on the Spoofing-Attack Finger Vein Database and the achieved results are presented in this paper.

Classification-based nonlocal SAR despeckling

Nonlocal techniques represent the current state of the art in SAR despeckling, providing a good compromise between speckle reduction and preservation of relevant image features. Nonetheless, they are not free from problems, going from the loss of image features to the introduction of their own brand of artifacts, due to the inability to deal equally well with all types of imaged scenes. A possible tool to improve performance is a prior segmentation or classification of the image, so as to adjust the filter parameters to fit the nature of the region under analysis. This work first provides some insight into the potential of classification-based nonlocal filtering by running simulation experiments in a controlled environment. Then proposes a new version of the SAR-BM3D despeckling technique in which each pixel is first classified as homogeneous or not, and then filtered with class-adapted parameters. Although results on real SAR images are still questionable, there is already some significant gain in selected areas that justifies the interest towards this approach.

Contact Lens Detection and Classification in Iris Images through Scale Invariant Descriptor

We propose a new machine-learning technique for detecting the presence and type of contact lenses in iris images. Following the usual paradigm, we extract the regions of interest for classification, compute a feature vector based on local descriptors, and feed it to a properly trained SVM classifier. Major improvements w.r.t. Current state of the art concern the design of a more reliable segmentation procedure and the use of a recently proposed dense scale-invariant image descriptor. Experiments on publicly available datasets show the proposed method to outperform significantly all reference techniques.

SAR Image Despeckling by Soft Classification

We propose a new approach to synthetic aperture radar (SAR) despeckling, based on the combination of multiple alternative estimates of the same data. The many despeckling methods proposed in the literature possess different and often complementary strengths and weaknesses. Given a reliable pixelwise characterization of the image, one can take advantage of this diversity by selecting the most appropriate combination of estimators for each image region. Following this paradigm, we develop a simple algorithm where only two state-of-the-art despeckling tools, characterized by complementary properties, are linearly combined. To ensure the smooth combination of contributes, thus avoiding new artifacts, we propose a novel soft classification method, where a basic estimate of homogeneity is improved through nonlocal and multiresolution processing steps. The results of a number of experiments conducted on both synthetic and real-world SAR images are very promising, thus confirming the potential of the proposed approach.

SAR despeckling based on soft classification

We propose a new approach to SAR despeckling, based on the combination of multiple alternative estimates of the same data. The many despeckling methods proposed in the literature possess different and often complementary strengths and weaknesses. Given a reliable pixel-wise classification of the image, one can take advantage of this diversity by selecting the more appropriate combination of estimators for each image region. We implement a simplified version of this approach, using soft classification and two state-of-the-art despeckling tools, with opposite properties, as basic estimators. Experiments on real-world high-resolution SAR images prove the effectiveness of the proposed technique and confirm the potential of the whole approach.