2,262 research outputs found

    SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

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    Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

    Sensing, interpreting, and anticipating human social behaviour in the real world

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    Low-level nonverbal social signals like glances, utterances, facial expressions and body language are central to human communicative situations and have been shown to be connected to important high-level constructs, such as emotions, turn-taking, rapport, or leadership. A prerequisite for the creation of social machines that are able to support humans in e.g. education, psychotherapy, or human resources is the ability to automatically sense, interpret, and anticipate human nonverbal behaviour. While promising results have been shown in controlled settings, automatically analysing unconstrained situations, e.g. in daily-life settings, remains challenging. Furthermore, anticipation of nonverbal behaviour in social situations is still largely unexplored. The goal of this thesis is to move closer to the vision of social machines in the real world. It makes fundamental contributions along the three dimensions of sensing, interpreting and anticipating nonverbal behaviour in social interactions. First, robust recognition of low-level nonverbal behaviour lays the groundwork for all further analysis steps. Advancing human visual behaviour sensing is especially relevant as the current state of the art is still not satisfactory in many daily-life situations. While many social interactions take place in groups, current methods for unsupervised eye contact detection can only handle dyadic interactions. We propose a novel unsupervised method for multi-person eye contact detection by exploiting the connection between gaze and speaking turns. Furthermore, we make use of mobile device engagement to address the problem of calibration drift that occurs in daily-life usage of mobile eye trackers. Second, we improve the interpretation of social signals in terms of higher level social behaviours. In particular, we propose the first dataset and method for emotion recognition from bodily expressions of freely moving, unaugmented dyads. Furthermore, we are the first to study low rapport detection in group interactions, as well as investigating a cross-dataset evaluation setting for the emergent leadership detection task. Third, human visual behaviour is special because it functions as a social signal and also determines what a person is seeing at a given moment in time. Being able to anticipate human gaze opens up the possibility for machines to more seamlessly share attention with humans, or to intervene in a timely manner if humans are about to overlook important aspects of the environment. We are the first to propose methods for the anticipation of eye contact in dyadic conversations, as well as in the context of mobile device interactions during daily life, thereby paving the way for interfaces that are able to proactively intervene and support interacting humans.Blick, Gesichtsausdrücke, Körpersprache, oder Prosodie spielen als nonverbale Signale eine zentrale Rolle in menschlicher Kommunikation. Sie wurden durch vielzählige Studien mit wichtigen Konzepten wie Emotionen, Sprecherwechsel, Führung, oder der Qualität des Verhältnisses zwischen zwei Personen in Verbindung gebracht. Damit Menschen effektiv während ihres täglichen sozialen Lebens von Maschinen unterstützt werden können, sind automatische Methoden zur Erkennung, Interpretation, und Antizipation von nonverbalem Verhalten notwendig. Obwohl die bisherige Forschung in kontrollierten Studien zu ermutigenden Ergebnissen gekommen ist, bleibt die automatische Analyse nonverbalen Verhaltens in weniger kontrollierten Situationen eine Herausforderung. Darüber hinaus existieren kaum Untersuchungen zur Antizipation von nonverbalem Verhalten in sozialen Situationen. Das Ziel dieser Arbeit ist, die Vision vom automatischen Verstehen sozialer Situationen ein Stück weit mehr Realität werden zu lassen. Diese Arbeit liefert wichtige Beiträge zur autmatischen Erkennung menschlichen Blickverhaltens in alltäglichen Situationen. Obwohl viele soziale Interaktionen in Gruppen stattfinden, existieren unüberwachte Methoden zur Augenkontakterkennung bisher lediglich für dyadische Interaktionen. Wir stellen einen neuen Ansatz zur Augenkontakterkennung in Gruppen vor, welcher ohne manuelle Annotationen auskommt, indem er sich den statistischen Zusammenhang zwischen Blick- und Sprechverhalten zu Nutze macht. Tägliche Aktivitäten sind eine Herausforderung für Geräte zur mobile Augenbewegungsmessung, da Verschiebungen dieser Geräte zur Verschlechterung ihrer Kalibrierung führen können. In dieser Arbeit verwenden wir Nutzerverhalten an mobilen Endgeräten, um den Effekt solcher Verschiebungen zu korrigieren. Neben der Erkennung verbessert diese Arbeit auch die Interpretation sozialer Signale. Wir veröffentlichen den ersten Datensatz sowie die erste Methode zur Emotionserkennung in dyadischen Interaktionen ohne den Einsatz spezialisierter Ausrüstung. Außerdem stellen wir die erste Studie zur automatischen Erkennung mangelnder Verbundenheit in Gruppeninteraktionen vor, und führen die erste datensatzübergreifende Evaluierung zur Detektion von sich entwickelndem Führungsverhalten durch. Zum Abschluss der Arbeit präsentieren wir die ersten Ansätze zur Antizipation von Blickverhalten in sozialen Interaktionen. Blickverhalten hat die besondere Eigenschaft, dass es sowohl als soziales Signal als auch der Ausrichtung der visuellen Wahrnehmung dient. Somit eröffnet die Fähigkeit zur Antizipation von Blickverhalten Maschinen die Möglichkeit, sich sowohl nahtloser in soziale Interaktionen einzufügen, als auch Menschen zu warnen, wenn diese Gefahr laufen wichtige Aspekte der Umgebung zu übersehen. Wir präsentieren Methoden zur Antizipation von Blickverhalten im Kontext der Interaktion mit mobilen Endgeräten während täglicher Aktivitäten, als auch während dyadischer Interaktionen mittels Videotelefonie

    Privacy-preserving and Privacy-attacking Approaches for Speech and Audio -- A Survey

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    In contemporary society, voice-controlled devices, such as smartphones and home assistants, have become pervasive due to their advanced capabilities and functionality. The always-on nature of their microphones offers users the convenience of readily accessing these devices. However, recent research and events have revealed that such voice-controlled devices are prone to various forms of malicious attacks, hence making it a growing concern for both users and researchers to safeguard against such attacks. Despite the numerous studies that have investigated adversarial attacks and privacy preservation for images, a conclusive study of this nature has not been conducted for the audio domain. Therefore, this paper aims to examine existing approaches for privacy-preserving and privacy-attacking strategies for audio and speech. To achieve this goal, we classify the attack and defense scenarios into several categories and provide detailed analysis of each approach. We also interpret the dissimilarities between the various approaches, highlight their contributions, and examine their limitations. Our investigation reveals that voice-controlled devices based on neural networks are inherently susceptible to specific types of attacks. Although it is possible to enhance the robustness of such models to certain forms of attack, more sophisticated approaches are required to comprehensively safeguard user privacy

    Managing heterogeneous cues in social contexts. A holistic approach for social interactions analysis

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    Une interaction sociale désigne toute action réciproque entre deux ou plusieurs individus, au cours de laquelle des informations sont partagées sans "médiation technologique". Cette interaction, importante dans la socialisation de l'individu et les compétences qu'il acquiert au cours de sa vie, constitue un objet d'étude pour différentes disciplines (sociologie, psychologie, médecine, etc.). Dans le contexte de tests et d'études observationnelles, de multiples mécanismes sont utilisés pour étudier ces interactions tels que les questionnaires, l'observation directe des événements et leur analyse par des opérateurs humains, ou l'observation et l'analyse à posteriori des événements enregistrés par des spécialistes (psychologues, sociologues, médecins, etc.). Cependant, de tels mécanismes sont coûteux en termes de temps de traitement, ils nécessitent un niveau élevé d'attention pour analyser simultanément plusieurs descripteurs, ils sont dépendants de l'opérateur (subjectivité de l'analyse) et ne peuvent viser qu'une facette de l'interaction. Pour faire face aux problèmes susmentionnés, il peut donc s'avérer utile d'automatiser le processus d'analyse de l'interaction sociale. Il s'agit donc de combler le fossé entre les processus d'analyse des interactions sociales basés sur l'homme et ceux basés sur la machine. Nous proposons donc une approche holistique qui intègre des signaux hétérogènes multimodaux et des informations contextuelles (données "exogènes" complémentaires) de manière dynamique et optionnelle en fonction de leur disponibilité ou non. Une telle approche permet l'analyse de plusieurs "signaux" en parallèle (où les humains ne peuvent se concentrer que sur un seul). Cette analyse peut être encore enrichie à partir de données liées au contexte de la scène (lieu, date, type de musique, description de l'événement, etc.) ou liées aux individus (nom, âge, sexe, données extraites de leurs réseaux sociaux, etc.) Les informations contextuelles enrichissent la modélisation des métadonnées extraites et leur donnent une dimension plus "sémantique". La gestion de cette hétérogénéité est une étape essentielle pour la mise en œuvre d'une approche holistique. L'automatisation de la capture et de l'observation " in vivo " sans scénarios prédéfinis lève des verrous liés à i) la protection de la vie privée et à la sécurité ; ii) l'hétérogénéité des données ; et iii) leur volume. Par conséquent, dans le cadre de l'approche holistique, nous proposons (1) un modèle de données complet préservant la vie privée qui garantit le découplage entre les méthodes d'extraction des métadonnées et d'analyse des interactions sociales ; (2) une méthode géométrique non intrusive de détection par contact visuel ; et (3) un modèle profond de classification des repas français pour extraire les informations du contenu vidéo. L'approche proposée gère des signaux hétérogènes provenant de différentes modalités en tant que sources multicouches (signaux visuels, signaux vocaux, informations contextuelles) à différentes échelles de temps et différentes combinaisons entre les couches (représentation des signaux sous forme de séries temporelles). L'approche a été conçue pour fonctionner sans dispositifs intrusifs, afin d'assurer la capture de comportements réels et de réaliser l'observation naturaliste. Nous avons déployé l'approche proposée sur la plateforme OVALIE qui vise à étudier les comportements alimentaires dans différents contextes de la vie réelle et qui est située à l'Université Toulouse-Jean Jaurès, en France.Social interaction refers to any interaction between two or more individuals, in which information sharing is carried out without any mediating technology. This interaction is a significant part of individual socialization and experience gaining throughout one's lifetime. It is interesting for different disciplines (sociology, psychology, medicine, etc.). In the context of testing and observational studies, multiple mechanisms are used to study these interactions such as questionnaires, direct observation and analysis of events by human operators, or a posteriori observation and analysis of recorded events by specialists (psychologists, sociologists, doctors, etc.). However, such mechanisms are expensive in terms of processing time. They require a high level of attention to analyzing several cues simultaneously. They are dependent on the operator (subjectivity of the analysis) and can only target one side of the interaction. In order to face the aforementioned issues, the need to automatize the social interaction analysis process is highlighted. So, it is a question of bridging the gap between human-based and machine-based social interaction analysis processes. Therefore, we propose a holistic approach that integrates multimodal heterogeneous cues and contextual information (complementary "exogenous" data) dynamically and optionally according to their availability or not. Such an approach allows the analysis of multi "signals" in parallel (where humans are able only to focus on one). This analysis can be further enriched from data related to the context of the scene (location, date, type of music, event description, etc.) or related to individuals (name, age, gender, data extracted from their social networks, etc.). The contextual information enriches the modeling of extracted metadata and gives them a more "semantic" dimension. Managing this heterogeneity is an essential step for implementing a holistic approach. The automation of " in vivo " capturing and observation using non-intrusive devices without predefined scenarios introduces various issues that are related to data (i) privacy and security; (ii) heterogeneity; and (iii) volume. Hence, within the holistic approach we propose (1) a privacy-preserving comprehensive data model that grants decoupling between metadata extraction and social interaction analysis methods; (2) geometric non-intrusive eye contact detection method; and (3) French food classification deep model to extract information from the video content. The proposed approach manages heterogeneous cues coming from different modalities as multi-layer sources (visual signals, voice signals, contextual information) at different time scales and different combinations between layers (representation of the cues like time series). The approach has been designed to operate without intrusive devices, in order to ensure the capture of real behaviors and achieve the naturalistic observation. We have deployed the proposed approach on OVALIE platform which aims to study eating behaviors in different real-life contexts and it is located in University Toulouse-Jean Jaurès, France

    Multi-biometric templates using fingerprint and voice

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    As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

    Privacy-Protecting Techniques for Behavioral Data: A Survey

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    Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved
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