This paper presents a new dataset called HUMBI - a large corpus of high fidelity models of behavioral signals in 3D from a diverse population measured by a massive multi-camera system. With our novel design of a portable imaging system (consists of 107 HD cameras), we collect human behaviors from 164 subjects across gender, ethnicity, age, and physical condition at a public venue. Using the multiview image streams, we reconstruct high fidelity models of five elementary parts: gaze, face, hands, body, and cloth. As a byproduct, the 3D model provides geometrically consistent image annotation via 2D projection, e.g., body part segmentation. This dataset is a significant departure from the existing human datasets that suffers from subject diversity. We hope the HUMBI opens up a new opportunity for the development for behavioral imaging.
Humans transmit and respond to many different behavioral signals such as gaze movement, facial expression, and body gestures when they interact with others @cite_10 @cite_25 . Effective signaling and interpretation of signals are the basis of successful social performance, for example, in business @cite_0 @cite_12 @cite_21 . Researchers have developed various computational models to measure, model, and predict the behavioral signals @cite_19 @cite_15 . Some behavioral signals such as hand-flapping, repeating sounds, and deficits of joint attention have shown to be early markers of the autistic spectrum disorder, and computational tools have been designed to detect these symptoms @cite_43 @cite_3 . The data of behavioral signals is the key enabling factor, which builds computational models. Here, we briefly review the existing datasets for gaze, face, hand, body, and cloth. These datasets are summarized in Table .
@cite_21 @cite_3 @cite_0 @cite_19 @cite_43 @cite_15 @cite_10 @cite_25 @cite_12
2152928398 171934365 2165734786 2167462312 2143554828 1493363822 2097128017 2156333962
Humans and other animals express power through open, expansive postures, and they express powerlessness through closed, contractive postures. But can these postures actually cause power? The results of this study confirmed our prediction that posing in high-power nonverbal displays (as opposed to low-power nonverbal displays) would cause neuroendocrine and behavioral changes for both male and female participants: High-power posers experienced elevations in testosterone, decreases in cortisol, and increased feelings of power and tolerance for risk; low-power posers exhibited the opposite pattern. In short, posing in displays of power caused advantaged and adaptive psychological, physiological, and behavioral changes, and these findings suggest that embodiment extends beyond mere thinking and feeling, to physiology and subsequent behavioral choices. That a person can, by assuming two simple 1-min poses, embody power and instantly become more powerful has real-world, actionable implications. Nonlinguistic social signals (e.g., tone of voice’) are often as important as linguistic content in predicting behavioural outcomes [1,2]. This paper describes four automated measure of such social signalling, and shows that they can be used to form powerful predictosr of objective and subjective outcomes in several important situations. Finally, it is argued that such signals are important determinants of social position. Although developers of communication-support tools have certainly tried to create products that support group thinking, they usually do so without adequately accounting for social context, so that all too often these systems are jarring and even downright rude. In fact, most people would agree that today's communication technology seems to be at war with human society. Technology must account for this by recognizing that communication is always socially situated and that discussions are not just words but part of a larger social dialogue. This web of social interaction forms a sort of collective intelligence; it is the unspoken shared understanding that enforces the dominance hierarchy and passes judgment about it. We have found nonlinguistic social signals to be particularly powerful for analyzing and predicting human behavior, sometimes exceeding even expert human capabilities. Psychologists have firmly established that social signals are a powerful determinant of human behavior and speculate that they might have evolved as a way to establish hierarchy and group cohesion. We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition. We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms. How can you know when someone is bluffing? Paying attention? Genuinely interested? The answer, writes Sandy Pentland in Honest Signals, is that subtle patterns in how we interact with other people reveal our attitudes toward them. These unconscious social signals are not just a back channel or a complement to our conscious language; they form a separate communication network. Biologically based "honest signaling," evolved from ancient primate signaling mechanisms, offers an unmatched window into our intentions, goals, and values. If we understand this ancient channel of communication, Pentland claims, we can accurately predict the outcomes of situations ranging from job interviews to first dates. Pentland, an MIT professor, has used a specially designed digital sensor worn like an ID badgea "sociometer"to monitor and analyze the back-and-forth patterns of signaling among groups of people. He and his researchers found that this second channel of communication, revolving not around words but around social relations, profoundly influences major decisions in our liveseven though we are largely unaware of it. Pentland presents the scientific background necessary for understanding this form of communication, applies it to examples of group behavior in real organizations, and shows how by "reading" our social networks we can become more successful at pitching an idea, getting a job, or closing a deal. Using this "network intelligence" theory of social signaling, Pentland describes how we can harness the intelligence of our social network to become better managers, workers, and communicators. The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence - the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement - in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing. In this research we examine whether conversational dynamics occurring within the first five minutes of a negotiation can predict negotiated outcomes. In a simulated employment negotiation, micro-coding conducted by a computer showed that activity level, conversational engagement, prosodic emphasis, and vocal mirroring predicted 30 of the variance in individual outcomes. The conversational dynamics associated with individual success among high-status parties were different from those associated with individual success among low-status parties. Results are interpreted in light of theory and research exploring the predictive power of "thin slices" (Ambady & Rosenthal, 1992). Implications include the development of new technology to diagnose and improve negotiation processes.