1,721,008 research outputs found
Automatic Face Image Tagging in Large Collections
No full textIn this chapter, the authors present some issues related to automatic face image tagging techniques. Their main purpose in user applications is to support the organization (indexing) and retrieval (or easy browsing) of images or videos in large collections. Their core modules include algorithms and strategies for handling very large face databases, mostly acquired in real conditions. As a background for understanding how automatic face tagging works, an overview about face recognition techniques is given, including both traditional approaches and novel proposed techniques for face recognition in uncontrolled settings. Moreover, some applications and the way they work are summarized, in order to depict the state of the art in this area of face recognition research. Actually, many of them are used to tag faces and to organize photo albums with respect to the person(s) presented in annotated photos. This kind of activity has recently expanded from personal devices to social networks, and can also significantly support more demanding tasks, such as automatic handling of large editorial collections for magazine publishing and archiving. Finally, a number of approaches to large-scale face datasets as well as some automatic face image tagging techniques are presented and compared. The authors show that many approaches, both in commercial and research applications, still provide only a semi-automatic solution for this problem
Exploring Architectural Choices and Emerging Challenges in Data Management for IoT: A Focus on Digital Innovation and Smart Cities
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Biometric 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
Autonomous Driving: Integration of Segmentation and Depth Camera in a Curriculum Learning Approach
No full textF-FID: fast fuzzy-based iris de-noising for mobile security applications
No full textOnce confined to indoor biometric applications depending on dedicated acquisition devices, recently the iris has proved to be a suitable biometric for in-the-wild ubiquitous person authentication, thanks to continuously improving image capturing/processing performances provided by last generations of smartphones. In this mobile context, the efficiency of the whole processing pipeline represents a crucial aspect of any practical application and the segmentation task, that is deeply affected by noisy iris images may become a serious bottleneck. This work presents F-FID, an effective and time-wise efficient approach to de-noising of iris images by means of a fuzzy controller without sacrificing their resolution and saliency. The experiments, specifically conducted on the MICHE dataset, confirm that the proposed method provides segmentation accuracy comparable to that achieved by state of the art algorithms, while requiring less than twenty percent of their average computing time
COMPLEX NUMBERS AS A COMPACT WAY TO REPRESENT SCORES AND THEIR RELIABILITY IN RECOGNITION BY MULTI-BIOMETRIC FUSION
No full textMulti-biometric systems are a powerful solution to deal with limitations of single classifiers, therefore improving the final recognition accuracy. The sub-systems composing the final architecture often return supplementary indices of input quality and/or of response reliability, which further qualify each recognition score. These indices can enter different information fusion policies. First, they can be used as weights for the fusion of the corresponding scores, in such a way that less trustworthy responses have a lower influence. Alternatively, they can be used to drive the selection of a subset of systems actually enabled for each fusion operation. The present work discusses their appropriate combination with respective scores, to obtain single values which are easier to handle and compare. It is worth underlining the different nature of quality and reliability measures. The quality estimation of input samples requires a complex analysis of environmental conditions, including capture sensors, besides computations over acquired data. Reliability of a system estimates its ability to return a correct response. As an alternative to combination, some solutions rather estimate the joint distributions of conditional probabilities of the scores from the single subsystems. These solutions require training through a huge number of samples. Furthermore, they assume stable score distributions. Our unified representation of the recognition score and of the corresponding quality/reliability value into a single complex number provides simplification and speed up of fusion of multi-classifier results. It also allows to devise procedures to readily compare the performance of different modules in a multi-biometric system, given that there is no natural ordering of these pairs of values of different nature. Moreover our method achieves performance comparable to top performing schemes, yet does not require a prior estimation of (joint) score distributions. As a matter of fact, though representing an upper bound to the obtainable performance, Likelihood ratio has the limit to require an accurate estimation of score distributions, while our approach relies on the reliability of each single response. This feature is very interesting when the set of relevant subjects may present significant variations over time
Fusion of physiological measures for multimodal biometric systems
No full textPhysiological 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
Artificial Intelligence Methods for Smart Cities
Full text linkIn recent years, the concept of smart cities has garnered increasing attention as urban areas grapple with the challenges of population growth, resource management, and infrastructure optimization [...
Continuous authentication on smartphone by means of periocular and virtual keystroke
No full textNowadays, biometric recognition and verification methods are everywhere, trying to face the security issues that constantly affect our digital-every day life. In addition, many special-purpose applications, also need a constant (continuous) verification of the user in order to avoid that a sensitive operation is executed by an impostor; as an example let think to banking operations. In this paper, a continuous authentication method on mobile device is presented, which uses smartphone gestures data for the constant verification of the user and periocular data for a second step verification module. The results executed over two datasets show a verification accuracy of 83% and 94% approximately, respectively for smartphone touch features and periocular data
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