temporal analysis of adaptive face recognition

Abstract Aging has profound effects on facial biometrics as it causes change in shape and texture. However, aging remains an under-studied problem in comparison to facial variations due to pose, illumination and expression changes. A commonly adopted solution in the state-of-the-art is the virtual template synthesis for aging and de-aging transformations involving complex 3D modelling techniques. These methods are also prone to estimation errors in the synthesis. Another viable solution is to continuously adapt the template to the temporal variation (ageing) of the query data. Though efficacy of template update procedures has been proven for expression, lightning and pose variations, the use of template update for facial aging has not received much attention so far. Therefore, this paper first analyzes the performance of existing baseline facial representations, based on local features, under ageing effect then investigates the use of template update procedures for temporal variance due to the facial age progression process. Experimental results on FGNET and MORPH aging database using commercial VeriLook face recognition engine demonstrate that continuous template updating is an effective and simple way to adapt to variations due to the aging process

Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements

Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments. To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o

QUEST Hierarchy for Hyperspectral Face Recognition

Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided

BIOMETRIC TECHNOLOGIES FOR AMBIENT INTELLIGENCE

Il termine Ambient Intelligence (AmI) si riferisce a un ambiente in grado di riconoscere e rispondere alla presenza di diversi individui in modo trasparente, non intrusivo e spesso invisibile. In questo tipo di ambiente, le persone sono circondate da interfacce uomo macchina intuitive e integrate in oggetti di ogni tipo. Gli scopi dell\u2019AmI sono quelli di fornire un supporto ai servizi efficiente e di facile utilizzo per accrescere le potenzialit\ue0 degli individui e migliorare l\u2019interazioni uomo-macchina. Le tecnologie di AmI possono essere impiegate in contesti come uffici (smart offices), case (smart homes), ospedali (smart hospitals) e citt\ue0 (smart cities). Negli scenari di AmI, i sistemi biometrici rappresentano tecnologie abilitanti al fine di progettare servizi personalizzati per individui e gruppi di persone. La biometria \ue8 la scienza che si occupa di stabilire l\u2019identit\ue0 di una persona o di una classe di persone in base agli attributi fisici o comportamentali dell\u2019individuo. Le applicazioni tipiche dei sistemi biometrici includono: controlli di sicurezza, controllo delle frontiere, controllo fisico dell\u2019accesso e autenticazione per dispositivi elettronici. Negli scenari basati su AmI, le tecnologie biometriche devono funzionare in condizioni non controllate e meno vincolate rispetto ai sistemi biometrici comunemente impiegati. Inoltre, in numerosi scenari applicativi, potrebbe essere necessario utilizzare tecniche in grado di funzionare in modo nascosto e non cooperativo. In questo tipo di applicazioni, i campioni biometrici spesso presentano una bassa qualit\ue0 e i metodi di riconoscimento biometrici allo stato dell\u2019arte potrebbero ottenere prestazioni non soddisfacenti. \uc8 possibile distinguere due modi per migliorare l\u2019applicabilit\ue0 e la diffusione delle tecnologie biometriche negli scenari basati su AmI. Il primo modo consiste nel progettare tecnologie biometriche innovative che siano in grado di funzionare in modo robusto con campioni acquisiti in condizioni non ideali e in presenza di rumore. Il secondo modo consiste nel progettare approcci biometrici multimodali innovativi, in grado di sfruttare a proprio vantaggi tutti i sensori posizionati in un ambiente generico, al fine di ottenere un\u2019elevata accuratezza del riconoscimento ed effettuare autenticazioni continue o periodiche in modo non intrusivo. Il primo obiettivo di questa tesi \ue8 la progettazione di sistemi biometrici innovativi e scarsamente vincolati in grado di migliorare, rispetto allo stato dell\u2019arte attuale, la qualit\ue0 delle tecniche di interazione uomo-macchine in diversi scenari applicativi basati su AmI. Il secondo obiettivo riguarda la progettazione di approcci innovativi per migliorare l\u2019applicabilit\ue0 e l\u2019integrazione di tecnologie biometriche eterogenee negli scenari che utilizzano AmI. In particolare, questa tesi considera le tecnologie biometriche basate su impronte digitali, volto, voce e sistemi multimodali. Questa tesi presenta le seguenti ricerche innovative: \u2022 un metodo per il riconoscimento del parlatore tramite la voce in applicazioni che usano AmI; \u2022 un metodo per la stima dell\u2019et\ue0 dell\u2019individuo da campioni acquisiti in condizioni non-ideali nell\u2019ambito di scenari basati su AmI; \u2022 un metodo per accrescere l\u2019accuratezza del riconoscimento biometrico in modo protettivo della privacy e basato sulla normalizzazione degli score biometrici tramite l\u2019analisi di gruppi di campioni simili tra loro; \u2022 un approccio per la fusione biometrica multimodale indipendente dalla tecnologia utilizzata, in grado di combinare tratti biometrici eterogenei in scenari basati su AmI; \u2022 un approccio per l\u2019autenticazione continua multimodale in applicazioni che usano AmI. Le tecnologie biometriche innovative progettate e descritte in questa tesi sono state validate utilizzando diversi dataset biometrici (sia pubblici che acquisiti in laboratorio), i quali simulano le condizioni che si possono verificare in applicazioni di AmI. I risultati ottenuti hanno dimostrato la realizzabilit\ue0 degli approcci studiati e hanno mostrato che i metodi progettati aumentano l\u2019accuratezza, l\u2019applicabilit\ue0 e l\u2019usabilit\ue0 delle tecnologie biometriche rispetto allo stato dell\u2019arte negli scenari basati su AmI.Ambient Intelligence (AmI) refers to an environment capable of recognizing and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way. In this environment, people are surrounded by intelligent intuitive interfaces that are embedded in all kinds of objects. The goals of AmI are to provide greater user-friendliness, more efficient services support, user-empowerment, and support for human interactions. Examples of AmI scenarios are smart cities, smart homes, smart offices, and smart hospitals. In AmI, biometric technologies represent enabling technologies to design personalized services for individuals or groups of people. Biometrics is the science of establishing the identity of an individual or a class of people based on the physical, or behavioral attributes of the person. Common applications include: security checks, border controls, access control to physical places, and authentication to electronic devices. In AmI, biometric technologies should work in uncontrolled and less-constrained conditions with respect to traditional biometric technologies. Furthermore, in many application scenarios, it could be required to adopt covert and non-cooperative technologies. In these non-ideal conditions, the biometric samples frequently present poor quality, and state-of-the-art biometric technologies can obtain unsatisfactory performance. There are two possible ways to improve the applicability and diffusion of biometric technologies in AmI. The first one consists in designing novel biometric technologies robust to samples acquire in noisy and non-ideal conditions. The second one consists in designing novel multimodal biometric approaches that are able to take advantage from all the sensors placed in a generic environment in order to achieve high recognition accuracy and to permit to perform continuous or periodic authentications in an unobtrusive manner. The first goal of this thesis is to design innovative less-constrained biometric systems, which are able to improve the quality of the human-machine interaction in different AmI environments with respect to the state-of-the-art technologies. The second goal is to design novel approaches to improve the applicability and integration of heterogeneous biometric systems in AmI. In particular, the thesis considers technologies based on fingerprint, face, voice, and multimodal biometrics. This thesis presents the following innovative research studies: \u2022 a method for text-independent speaker identification in AmI applications; \u2022 a method for age estimation from non-ideal samples acquired in AmI scenarios; \u2022 a privacy-compliant cohort normalization technique to increase the accuracy of already deployed biometric systems; \u2022 a technology-independent multimodal fusion approach to combine heterogeneous traits in AmI scenarios; \u2022 a multimodal continuous authentication approach for AmI applications. The designed novel biometric technologies have been tested on different biometric datasets (both public and collected in our laboratory) simulating the acquisitions performed in AmI applications. Results proved the feasibility of the studied approaches and shown that the studied methods effectively increased the accuracy, applicability, and usability of biometric technologies in AmI with respect to the state-of-the-art

Adaptive classifier ensembles for face recognition in video-surveillance

Lors de l’implémentation de systèmes de sécurité tels que la vidéo-surveillance intelligente, l’utilisation d’images de visages présente de nombreux avantages par rapport à d’autres traits biométriques. En particulier, cela permet de détecter d’éventuels individus d’intérêt de manière discrète et non intrusive, ce qui peut être particulièrement avantageux dans des situations comme la détection d’individus sur liste noire, la recherche dans des données archivées ou la ré-identification de visages. Malgré cela, la reconnaissance de visages reste confrontée à de nombreuses difficultés propres à la vidéo surveillance. Entre autres, le manque de contrôle sur l’environnement observé implique de nombreuses variations dans les conditions d’éclairage, la résolution de l’image, le flou de mouvement, l’orientation et l’expression des visages. Pour reconnaître des individus, des modèles de visages sont habituellement générés à l’aide d’un nombre limité d’images ou de vidéos de référence collectées lors de sessions d’inscription. Cependant, ces acquisitions ne se déroulant pas nécessairement dans les mêmes conditions d’observation, les données de référence représentent pas toujours la complexité du problème réel. D’autre part, bien qu’il soit possible d’adapter les modèles de visage lorsque de nouvelles données de référence deviennent disponibles, un apprentissage incrémental basé sur des données significativement différentes expose le système à un risque de corruption de connaissances. Enfin, seule une partie de ces connaissances est effectivement pertinente pour la classification d’une image donnée. Dans cette thèse, un nouveau système est proposé pour la détection automatique d’individus d’intérêt en vidéo-surveillance. Plus particulièrement, celle-ci se concentre sur un scénario centré sur l’utilisateur, où un système de reconnaissance de visages est intégré à un outil d’aide à la décision pour alerter un opérateur lorsqu’un individu d’intérêt est détecté sur des flux vidéo. Un tel système se doit d’être capable d’ajouter ou supprimer des individus d’intérêt durant son fonctionnement, ainsi que de mettre à jour leurs modèles de visage dans le temps avec des nouvelles données de référence. Pour cela, le système proposé se base sur de la détection de changement de concepts pour guider une stratégie d’apprentissage impliquant des ensembles de classificateurs. Chaque individu inscrit dans le système est représenté par un ensemble de classificateurs à deux classes, chacun étant spécialisé dans des conditions d’observation différentes, détectées dans les données de référence. De plus, une nouvelle règle pour la fusion dynamique d’ensembles de classificateurs est proposée, utilisant des modèles de concepts pour estimer la pertinence des classificateurs vis-à-vis de chaque image à classifier. Enfin, les visages sont suivis d’une image à l’autre dans le but de les regrouper en trajectoires, et accumuler les décisions dans le temps. Au Chapitre 2, la détection de changement de concept est dans un premier temps utilisée pour limiter l’augmentation de complexité d’un système d’appariement de modèles adoptant une stratégie de mise à jour automatique de ses galeries. Une nouvelle approche sensible au contexte est proposée, dans laquelle seules les images de haute confiance capturées dans des conditions d’observation différentes sont utilisées pour mettre à jour les modèles de visage. Des expérimentations ont été conduites avec trois bases de données de visages publiques. Un système d’appariement de modèles standard a été utilisé, combiné avec un module de détection de changement dans les conditions d’illumination. Les résultats montrent que l’approche proposée permet de diminuer la complexité de ces systèmes, tout en maintenant la performance dans le temps. Au Chapitre 3, un nouveau système adaptatif basé des ensembles de classificateurs est proposé pour la reconnaissance de visages en vidéo-surveillance. Il est composé d’un ensemble de classificateurs incrémentaux pour chaque individu inscrit, et se base sur la détection de changement de concepts pour affiner les modèles de visage lorsque de nouvelles données sont disponibles. Une stratégie hybride est proposée, dans laquelle des classificateurs ne sont ajoutés aux ensembles que lorsqu’un changement abrupt est détecté dans les données de référence. Lors d’un changement graduel, les classificateurs associés sont mis à jour, ce qui permet d’affiner les connaissances propres au concept correspondant. Une implémentation particulière de ce système est proposée, utilisant des ensembles de classificateurs de type Fuzzy-ARTMAP probabilistes, générés et mis à jour à l’aide d’une stratégie basée sur une optimisation par essaims de particules dynamiques, et utilisant la distance de Hellinger entre histogrammes pour détecter des changements. Les simulations réalisées sur la base de donnée de vidéo-surveillance Faces in Action (FIA) montrent que le système proposé permet de maintenir un haut niveau de performance dans le temps, tout en limitant la corruption de connaissance. Il montre des performances de classification supérieure à un système similaire passif (sans détection de changement), ainsi qu’a des systèmes de référence de type kNN probabiliste, et TCM-kNN. Au Chapitre 4, une évolution du système présenté au Chapitre 3 est proposée, intégrant des mécanismes permettant d’adapter dynamiquement le comportement du système aux conditions d’observation changeantes en mode opérationnel. Une nouvelle règle de fusion basée sur de la pondération dynamique est proposée, assignant à chaque classificateur un poids proportionnel à son niveau de compétence estimé vis-à-vis de chaque image à classifier. De plus, ces compétences sont estimées à l’aide des modèles de concepts utilisés en apprentissage pour la détection de changement, ce qui permet un allègement des ressources nécessaires en mode opérationnel. Une évolution de l’implémentation proposée au Chapitre 3 est présentée, dans laquelle les concepts sont modélisés à l’aide de l’algorithme de partitionnement Fuzzy C-Means, et la fusion de classificateurs réalisée avec une moyenne pondérée. Les simulation expérimentales avec les bases de données de vidéo-surveillance FIA et Chokepoint montrent que la méthode de fusion proposée permet d’obtenir des résultats supérieurs à la méthode de sélection dynamique DSOLA, tout en utilisant considérablement moins de ressources de calcul. De plus, la méthode proposée montre des performances de classification supérieures aux systèmes de référence de type kNN probabiliste, TCM-kNN et Adaptive Sparse Coding

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about