Daniel Riccio

FIRME: Face and Iris Recognition for Mobile Engagement☆

Abstract Mobile devices, namely phones and tablets, have long gone “smart”. Their growing use is both a cause and an effect of their technological advancement. Among the others, their increasing ability to store and exchange sensitive information, has caused interest in exploiting their vulnerabilities, and the opposite need to protect users and their data through secure protocols for access and identification on mobile platforms. Face and iris recognition are especially attractive, since they are sufficiently reliable, and just require the webcam normally equipping the involved devices. On the contrary, the alternative use of fingerprints requires a dedicated sensor. Moreover, some kinds of biometrics lend themselves to uses that go beyond security. Ambient intelligence services bound to the recognition of a user, as well as social applications, such as automatic photo tagging on social networks, can especially exploit face recognition. This paper describes FIRME (Face and Iris Recognition for Mobile Engagement) as a biometric application based on a multimodal recognition of face and iris, which is designed to be embedded in mobile devices. Both design and implementation of FIRME rely on a modular architecture, whose workflow includes separate and replaceable packages. The starting one handles image acquisition. From this point, different branches perform detection, segmentation, feature extraction, and matching for face and iris separately. As for face, an antispoofing step is also performed after segmentation. Finally, results from the two branches are fused. In order to address also security-critical applications, FIRME can perform continuous reidentification and best sample selection. To further address the possible limited resources of mobile devices, all algorithms are optimized to be low-demanding and computation-light.

Mobile Iris Challenge Evaluation (MICHE)-I, biometric iris dataset and protocols

A new dataset of iris images acquired by mobile devices can support researchers.MICHE-I will assist with developing continuous authentication to counter spoofing.The dataset includes images from different mobile devices, sessions and conditions. We introduce and describe here MICHE-I, a new iris biometric dataset captured under uncontrolled settings using mobile devices. The key features of the MICHE-I dataset are a wide and diverse population of subjects, the use of different mobile devices for iris acquisition, realistic simulation of the acquisition process (including noise), several data capture sessions separated in time, and image annotation using metadata. The aim of MICHE-I dataset is to make up the starting core of a wider dataset that we plan to collect, with the further aim to address interoperability, both in the sense of matching samples acquired with different devices and of assessing the robustness of algorithms to the use of devices with different characteristics. We discuss throughout the merits of MICHE-I with regard to biometric dimensions of interest including uncontrolled settings, demographics, interoperability, and real-world applications. We also consider the potential for MICHE-I to assist with developing continuous authentication aimed to counter adversarial spoofing and impersonation, when the bar for uncontrolled settings raises even higher for proper and effective defensive measures.

BIRD: Watershed Based IRis Detection for mobile devices ✩

Communications with a central iris database system using common wireless technologies, such as tablets and smartphones, and iris acquisition out of the field are important functionalities and capabilities of a mobile iris identification device. However, when images are acquired by means of mobile devices under uncontrolled acquisition conditions, noisy images are produced and the effectiveness of the iris recognition system is significantly conditioned. This paper proposes a technique based on watershed transform for iris detection in noisy images captured by mobile devices. The method exploits the information related to limbus to segment the periocular region and merges its score with the iris’ one to achieve greater accuracy in the recognition phase.

WIRE: Watershed based iris recognition

Abstract A Watershed transform based Iris REcognition system (WIRE) for noisy images acquired in visible wavelength is presented. Key points of the system are: the color/illumination correction pre-processing step, which is crucial for darkly pigmented irises whose albedo would be dominated by corneal specular reflections; the criteria used for the binarization of the watershed transform, leading to a preliminary segmentation which is refined by taking into account the watershed regions at least partially included in the best iris fitting circle; the introduction of a new cost function to score the circles detected as potentially delimiting limbus and pupil. The advantage offered by the high precision of WIRE in iris segmentation has a positive impact as regards the iris code, which results to be more accurately computed, so that the performance of iris recognition is also improved. To assess the performance of WIRE and to compare it with the performance of other available methods, two well known databases have been used, specifically UBIRIS version 1 session 2 and the subset of UBIRIS version 2 that has been used as training set for the international challenge NICE II.

Robust face recognition after plastic surgery using region-based approaches

This paper advances the use of region-based strategies for addressing the problem of face recognition after plastic surgery. The proposed methods implement the region-based approach in several ways. FARO (FAce Recognition against Occlusions and Expression Variations) divides the face into relevant regions (left eye, right eye, nose and mouth) and then codes them independently using Partitioned Iterated Function System (PIFS) processing. FACE (Face Analysis for Commercial Entities) applies a localized version of image correlation index. Finally, the Split Face Architecture (SFA), adaptive and integrative in nature, can leverage any known recognition method, from PCA to most recent ones (including FARO and FACE), provided that it is possible to divide the face into regions. Experimental results, compared with those available from recent experiments reported in literature, show that our methods yield much better performance than state-of-the art algorithms, both holistic and region based. We compare region-based approaches in addressing face recognition after plastic surgery.We exploit a split face architecture to address face recognition as a multibiometric problem.We propose collaboration among component modules of a multibiometric system.We implement a supervisor module to coordinate feedback from any response.

MEG: Texture operators for multi-expert gender classification

Abstract In this paper we focus on gender classification from face images. Despite advances in equipment as well as methods, automatic face image processing for recognition or even just for the extraction of demographics, is still a challenging task in unrestricted scenarios. Our tests are aimed at carrying out an extensive comparison of a feature based approach with two score based ones. When directly using features, we first apply different operators to extract the corresponding feature vectors, and then stack such vectors. These are classified by a SVM-based approach. When using scores, the different operators are applied in a completely separate way, so that each of them produces the corresponding scores. Answers are then either fed to a SVM, or compared pairwise to exploit Likelihood Ratio. The testbeds used for experiments are EGA database, which presents a good balance with respect to demographic features of stored face images, and GROPUS, an increasingly popular benchmark for massive experiments. The obtained performances confirm that feature level fusion achieves an often better classification accuracy. However, it is computationally expensive. We contribute to the research on this topic in three ways: 1) we show that the proposed score level fusion approaches, though less demanding, can achieve results that are comparable to feature level fusion, or even slightly better given that we fuse a particular set of experts; the main advantage over the feature-based approach relying on chained vectors, is that it is not required to evaluate a complex multi-feature distribution and the training process: thanks to the individual training of experts the overall process is more efficient and flexible, since experts can be easily added or discarded from the final architecture; 2) we evaluate the number of uncertain/ambiguous cases, i.e., those that might cause classification errors depending on the classification thresholds used, and show that with our score level fusion these significantly decreases; despite the final rate of correct classifications, this results in a more robust system; 3) we achieve very good results with operators that are not computationally expensive.

Severe: Segmenting vessels in retina images ☆

Abstract This paper presents the unsupervised retinal vessels segmentation method, SEVERE (SEgmenting VEssels in REtina images), which is based on the direction map of retina scan images assigning each pixel one out of twelve discrete directions. SEVERE works on the green channel of RGB retina scan images. It does not require any pre-processing phase and all the computations are done exclusively on the direction map. SEVERE has been checked on publicly available datasets producing qualitatively satisfactory results and outperforming other existing methods in terms of quantitative performance evaluation parameters, such as accuracy and sensitivity.

IDEM: Iris DEtection on Mobile Devices

In this paper an iris detection scheme for noisy images acquired by means of mobile devices is presented. Iris segmentation is accomplished by exploiting the use of the watershed transform with the purpose of identifying the iris boundary as much precisely as possible. After a pre-processing step aimed at color/illumination correction, the watershed transform is computed and suitably binarized. Circle fitting is then accomplished to identify the limbus boundary by using curvature approximation and a cost function for circle scoring. The watershed transform is furthermore employed to distinguish, in the zone delimited by the best fitting circle, the regions actually belonging to the iris from those belonging to eyelids and sclera. Finally, pupil detection is accomplished by means of circle fitting and by using a voting function based on homogeneity and separability criteria. The suggested iris detection scheme has a positive impact on an the accuracy in computing the iris code, which has in turn a positive impact on the performance of iris recognition.

GETSEL: Gallery entropy for template selection on large datasets

The ability of a biometric system to reliably recognize registered individuals significantly depends on the kind and amount of variation that the exploited biometric trait may undergo throughout acquisitions. Those variations may be due both to acquisition devices, or to different environment settings, or to modification of the trait appearance. One of the strategies to address changes in biometric features is to store more templates for the same person, in order to increase the chances to identify her. The problem arises to choose the templates to store in a way which actually achieves better performance, while avoiding flooding the system with an excessively huge gallery. This work proposes an approach for the selection of the best templates for face recognition, which is based on a notion of gallery entropy, and can be also used for large datasets. Though relying on a clustering process, its main achievement is to automatically derive the best number of clusters/prototypes per subject without requiring to fixing it in advance. Comparative tests with existing approaches show that it is a very promising solution.

Flexible and robust Enterprise Right Management

This paper presents and discusses an Enterprise Rights Management System (ERM) tailored for remote maintenance facilities which features secure on-site documentation storage and availability, strong data provider control over access policy and their enforcement, minimal documentation disclosure and strong and ergonomic operator authentication by means of biometric measurements. The design approach pursues the technological neutrality with respect to the documentation format and operator side equipments.