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Guest editorial introduction to the special executable issue on "Mobile Iris Challenge Evaluation part i (MICHE I)"
No full textBiometric recognition in surveillance scenarios: a survey
No full textInterest 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
Results from MICHE II – Mobile Iris CHallenge Evaluation II
Full text linkMobile biometrics represent the new frontier of authentication. The most appealing feature of mobile devices is the wide availability and the presence of more and more reliable sensors for capturing biometric traits, e.g., cameras and accelerometers. Moreover, they more and more often store personal and sensitive data, that need to be protected. Doing this on the same device using biometrics to enforce security seems a natural solution. This makes this research topic attracting and generally promising. However, the growing interest for related applications is counterbalanced by still present limitations, especially for some traits. Acquisition and computation resources are nowadays widely available, but they are not always sufficient to allow a reliable recognition result. Most of all, the way capture is expected to be carried out, i.e., by the user him/herself in uncontrolled conditions and without an expert assistance, can heavily affect the quality of samples and, as a consequence, the accuracy of recognition. Among the biometric traits raising the interest of researchers, iris plays an important role. Mobile Iris CHallenge Evaluation II (MICHE II) competition provided a testbed to assess the progress of mobile iris recognition, as well as its limitations still to overcome. This paper presents the results of the competition and the analysis of achieved performance, that takes into account both proposals submitted for the competition section launched at the 2016 edition of the International Conference on Pattern Recognition (ICPR), as well as proposals submitted for this special issue
Human Recognition in Unconstrained Environments: Using Computer Vision, Pattern Recognition and Machine Learning methods for Biometrics
No full textReal Time 3D Face-Ear Recognition on Mobile Devices: New Scenarios for 3D Biometrics “in-the-Wild”
No full textThe recent availability of compact and inexpensive depth cameras or the combination of high frame-rate cameras aboard the last generation of smartphones and tablets with computer vision techniques is enabling new applicative scenarios for 3D biometrics. In this chapter, new methods for performing real time 3D face and ear acquisition for biometric purposes, by means of non-specialized devices coupled with computer vision algorithms, are presented and discussed. To this regard, algorithms addressing the Synchronous Location and Mapping (SLAM) problem, like Parallel Tracking and Mapping (PTAM) and Dynamic Tracking and Mapping (DTAM) or the more recent MonoFusion and MobileFusion could make 3D biometrics much more exploitable than in the past, allowing even a not trained user to capture 3D facial features on the fly for improved recognition accuracy and/or reliability. This chapter also highlights the relevance of an efficient implementation of these inherently computationally intensive techniques, which can greatly benefit from GPU-based parallel processing, in making these solutions approachable in real-time on mobile hardware architectures. To this aim, the robustness of recognition algorithms to lower resolution, noisy face geometry, would result in a very desirable feature
Pattern Recognition Letters, Volume Volume 57, Special Issue on Mobile Iris CHallenge Evaluation part I (MICHE I)
No full text.). Pattern Recognition Letters, Volume 91 - Special Issue on Mobile Iris CHallenge Evaluation (MICHE-II)
No full textInsights into the results of MICHE I - Mobile Iris CHallenge Evaluation
Full text linkMobile biometrics technologies are nowadays the new frontier for secure use of data and services, and are considered particularly important due to the massive use of handheld devices in the entire world. Among the biometric traits with potential to be used in mobile settings, the iris/ocular region is a natural candidate, even considering that further advances in the technology are required to meet the operational requirements of such ambitious environments. Aiming at promoting these advances, we organized the Mobile Iris Challenge Evaluation (MICHE)-I contest. This paper presents a comparison of the performance of the participant methods by various Figures of Merit (FoMs). A particular attention is devoted to the identification of the image covariates that are likely to cause a decrease in the performance levels of the compared algorithms. Among these factors, interoperability among different devices plays an important role. The methods (or parts of them) implemented by the analyzed approaches are classified into segmentation (S), which was the main target of MICHE-I, and recognition (R). The paper reports both the results observed for either S or R, and also for different recombinations (S+R) of such methods. Last but not least, we also present the results obtained by multi-classifier strategies
Acquiring high-resolution face images in outdoor environments: A master-slave calibration algorithm
No full textFacial 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
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