Silvio Barra

Ubiquitous iris recognition by means of mobile devices

Iris authentication/recognition on mobile devices is feasible.Spatial histograms can be exploited for iris features extraction and matching.Performance of iris segmentation/recognition algorithms is strongly affected by capture conditions.Imaging sensors resolution alone does not necessarily result in higher recognition accuracy. The worldwide diffusion of latest generations mobile devices, namely smartphones and tablets, represents the technological premise to a new wave of applications for which reliable owner identification is becoming a key requirement. This crucial task can be approached by means of biometrics (face, iris or fingerprint) by exploiting high resolution imaging sensors typically built-in on this class of devices, possibly resulting in a ubiquitous platform to verify owner identity during any kind of transaction involving the exchange of sensible data. Among the aforementioned biometrics, iris is known for its inherent invariance and accuracy, though only a few works have explored this topic on mobile devices. In this paper a comprehensive method for iris authentication on mobiles by means of spatial histograms is described. The proposed approach has been tested on the MICHE-I iris dataset, featuring subjects captured indoor and outdoor under controlled and uncontrolled conditions by means of built-in cameras aboard three among the most diffused smartphones/tablets on the market. The experimental results collected, provide an interesting insight about the readiness of mobile technology with regard to iris recognition.

Biometric recognition in surveillance scenarios: a survey

Interest in the security of individuals has increased in recent years. This increase has in turn led to much wider deployment of surveillance cameras worldwide, and consequently, automated surveillance systems research has received more attention from the scientific community than before. Concurrently, biometrics research has become more popular as well, and it is supported by the increasing number of approaches devised to address specific degradation factors of unconstrained environments. Despite these recent efforts, no automated surveillance system that performs reliable biometric recognition in such an environment has become available. Nevertheless, recent developments in human motion analysis and biometric recognition suggest that both can be combined to develop a fully automated system. As such, this paper reviews recent advances in both areas, with a special focus on surveillance scenarios. When compared to previous studies, we highlight two distinct features, i.e., (1) our emphasis is on approaches that are devised to work in unconstrained environments and surveillance scenarios; and (2) biometric recognition is the final goal of the surveillance system, as opposed to behavior analysis, anomaly detection or action recognition.

Acquiring high-resolution face images in outdoor environments: A master-slave calibration algorithm

Facial recognition at-a-distance in surveillance scenarios remains an open problem, particularly due to the small number of pixels representing the facial region. The use of pan-tilt-zoom (PTZ) cameras has been advocated to solve this problem, however, the existing approaches either rely on rough approximations or additional constraints to estimate the mapping between image coordinates and pan-tilt parameters. In this paper, we aim at extending PTZ-assisted facial recognition to surveillance scenarios by proposing a master-slave calibration algorithm capable of accurately estimating pan-tilt parameters without depending on additional constraints. Our approach exploits geometric cues to automatically estimate subjects height and thus determine their 3D position. Experimental results show that the presented algorithm is able to acquire high-resolution face images at a distance ranging from 5 to 40 meters with high success rate. Additionally, we certify the applicability of the aforementioned algorithm to biometric recognition through a face recognition test, comprising 20 probe subjects and 13,020 gallery subjects.

FAME: Face Authentication for Mobile Encounter

The use of mobile devices is continuously growing, together with their ability to store and exchange sensitive data. This has spurred the interest, from one side, in exploiting their apparent vulnerabilities, and on the other side, in protecting users from any malfeasance. In this scenario, it has become essential to develop and deploy secure access and identification protocols on mobile platforms. Another emerging aspect that also needs attention bears on the commercial and social use of identity management systems. Towards such ends, this paper proposes using biometrics as the technology of choice, and describes FAME (Face Authentication for Mobile Encounters). FAME is an embedded application for mobile devices that provides both verification and identification, including identity management to support social activities, e.g., finding doubles in a social network. Such functionalities are implemented subject to privacy and security concerns. FAME cascade architecture is modular. The single modules perform image acquisition, anti-spoofing, face detection, face segmentation, feature extraction, and face matching. FAME also provides continuous recognition and best biometric sample selection, that both accommodate varying pose and illumination. All procedures are optimized for mobile use, using low-demanding and computation-light algorithms. Preliminary questionnaires administered to users engaged with using FAME show user acceptance, ease of use, and reliability.

Quis-Campi: Extending in the Wild Biometric Recognition to Surveillance Environments

Efforts in biometrics are being held into extending robust recognition techniques to in the wild scenarios. Nonetheless, and despite being a very attractive goal, human identification in the surveillance context remains an open problem. In this paper, we introduce a novel biometric system – Quis-Campi – that effectively bridges the gap between surveillance and biometric recognition while having a minimum amount of operational restrictions. We propose a fully automated surveillance system for human recognition purposes, attained by combining human detection and tracking, further enhanced by a PTZ camera that delivers data with enough quality to perform biometric recognition. Along with the system concept, implementation details for both hardware and software modules are provided, as well as preliminary results over a real scenario.

Fusion of physiological measures for multimodal biometric systems

Physiological measures are widely studied from a medical point of view. Most applications lie in the field of diagnosis of heart attacks, as regards the ECG, or the detection of epileptic events, in the case of the EEG. In the last ten years, these signals are being investigated also from a biometric point of view, in order to exploit the discriminative capability provided by these measures in recognizing individuals. The present work proposes a multimodal biometric recognition system based on the fusion of the first lead (i) of the electrocardiogram (ECG) with six different bands of the electroencephalogram (EEG). The proposed approach is based on the extraction of fiducial features (peaks) from the ECG combined with spectrum features of the EEG. A dataset has been created, by composing the signals of two well-known databases. The results, reported by means of EER values, AUC values and ROC curves, show good recognition performances.

Kurtosis and skewness at pixel level as input for SOM networks to iris recognition on mobile devices

Statistical operators of Kurtosis and Skewness at pixel level for iris recognition.Self Organizing Map (SOM) for clustering pixels of iris images.The size of the network does not significantly impact on recognition performances.Environmental noise critically affects the achievable recognition rate on mobile. The increasing popularity of smartphones amongst the population laid the basis for a wide range of applications aimed at security and privacy protection. Very modern mobile devices have recently demonstrated the feasibility of using a camera sensor to access the system without typing any alphanumerical password. In this work, we present a method that implements iris recognition in the visible spectrum through unsupervised learning by means of Self Organizing Maps (SOM). The proposed method uses a SOM network to cluster iris features at pixel level. The discriminative feature map is obtained by using RGB data of the iris combined with the statistical descriptors of kurtosis and skewness. An experimental analysis on MICHE-I and UBIRISv1 datasets demonstrates the strengths and weaknesses of the algorithm, which has been specifically designed to require low processing power in compliance with the limited capability of common mobile devices.

Unconstrained Ear Processing: What is Possible and What Must Be Done

Ear biometrics, compared with other physical traits, presents both advantages and limits. First of all, the small surface and the quite simple structure play a controversial role. On the positive side, they allow faster processing than, say, face recognition, as well as less complex recognition strategies than, say, fingerprints. On the negative side, the small ear area itself makes recognition systems especially sensitive to occlusions. Moreover, the prominent 3D structure of distinctive elements like the pinna and the lobe makes the same systems sensible to changes in illumination and viewpoint. Overall, the best accuracy results are still achieved in conditions that are significantly more favorable than those found in typical (really) uncontrolled settings. This makes the use of this biometrics in real world applications still difficult to propose, since a commercial use requires a much higher robustness. Notwithstanding the mentioned limits, ear is still an attractive topic for biometrics research, due to other positive aspects. In particular, it is quite easy to acquire ear images remotely, and these anatomic features are also relatively stable in size and structure along time. Of course, as any other biometric trait, they also call for some template updating. This is mainly due to age, but not in the commonly assumed way. The apparent bigger size of elders’ ears with respect to those of younger subjects, is due to the fact that aging causes a relaxation of the skin and of some muscle-fibrous structures that hold the so called pinna, i.e. the most evident anatomical element of the ear. This creates the belief that ears continue growing all life long. On the other hand, a similar process holds for the nose, for which the relaxation of the cartilage tissue tends to cause a curvature downwards. In this chapter we will present a survey of present techniques for ear recognition, from geometrical to 2D-3D multimodal, and will attempt a reasonable hypothesis about the future ability of ear biometrics to fulfill the requirements of less controlled/covert data acquisition frameworks.

SKIPSOM: Skewness & kurtosis of iris pixels in Self Organizing Maps for iris recognition on mobile devices

In the last fifteen years, smartphones have become very popular amongst the population, with the subsequent development of dozens of applications aimed at providing security to these portable devices. Nowadays, the cutting edge devices are also provided with biometric sensors (e.g., fingerprint sensors) allowing the users to access them without using the out-of-date alphanumerical password. In this work, we present a method that realizes iris recognition by means of Self Organizing Maps (SOM). In order to obtain a better refined and discriminative feature map, the RGB data of the iris, previously segmented, have been combined with two statistical descriptors. The algorithm has been designed specifically to require a low processing power, making it an ideal choice in the context of mobile devices.

Joint Head Pose/Soft Label Estimation for Human Recognition In-The-Wild

Soft biometrics have been emerging to complement other traits and are particularly useful for poor quality data. In this paper, we propose an efficient algorithm to estimate human head poses and to infer soft biometric labels based on the 3D morphology of the human head. Starting by considering a set of pose hypotheses, we use a learning set of head shapes synthesized from anthropometric surveys to derive a set of 3D head centroids that constitutes a metric space. Next, representing queries by sets of 2D head landmarks, we use projective geometry techniques to rank efficiently the joint 3D head centroids/pose hypotheses according to their likelihood of matching each query. The rationale is that the most likely hypotheses are sufficiently close to the query, so a good solution can be found by convex energy minimization techniques. Once a solution has been found, the 3D head centroid and the query are assumed to have similar morphology, yielding the soft label. Our experiments point toward the usefulness of the proposed solution, which can improve the effectiveness of face recognizers and can also be used as a privacy-preserving solution for biometric recognition in public environments.