Abstract: We propose a sensor-based organisational design and engineering (ODE) approach that combines behavioural sensor data with other sources of information such as e-mail, surveys and performance data in order to design interventions aimed at improving organisational outcomes. We discuss relevant theory and technology backgrounds and describe the general requirements of a sensor-based organisational design and engineering system. We present an experimental platform that combines sensor measurements, pattern recognition algorithms, statistical analysis, social network analysis and feedback mechanisms to study the relationship between social signalling behaviour and face-to-face (f2f) interaction networks, with job attitudes and performance. We describe three case studies that we have conducted in several organisations using our experimental platform and the methodology that we have followed. The first study looks at e-mail and f2f networks in a marketing division of a bank. The second study analyses the effects of nurses ’ social behaviour on patients ’ length of stay in the post-anaesthesia care unit of a hospital. Finally, the third study analyses the effects of retail bank employees ’ social behaviour on sales performance.

This study is to investigate the fundamental problems of, (1) facial feature detection and localization, especially eye features; and (2) eye dynamics, including tracking and blink detection. We first describe our contribution to eye localization. Following that, we discuss a simultaneous eye tracking and blink detection system. Facial feature detection is solved in a general object detection framework and its performance for eye localization is presented. A binary tree representation based on feature dependency partitions the object feature space in a coarse to fine manner. In each compact feature subspace, independent component analysis (ICA) is used to get the independent sources, whose probability density functions (PDFs) are modeled by Gaussian mixtures. When applying this representation for the task of eye detection, a subwindow is used to scan the entire image and each obtained image patch is examined using Bayesian criteria to determine the presence of an eye subject. After the eyes are automatically located with binary tree-based probability learning, interactive particle filters are used for simultaneously tracking the eyes and detecting the blinks. The particle filters use classification-based observation models, in which the posterior probabilities are evaluated by logistic regressions in tensor subspaces. Extensive experiments are used to evaluate the performance from two aspects, (1) blink detection rate and the accuracy of blink duration in terms of the frame numbers; (2) eye tracking accuracy. We also present an experimental setup for obtaining the benchmark data in tracking accuracy evaluation. The experimental evaluation demonstrates the capability of this approach.

WE HAVE entered an era of enhanced digital connectivity. Computers and the Internet have become so embedded in the daily fabric of people’s lives that people simply cannot live without them. We use this technology to work, to communicate, to shop, to seek out new information, and to entertain ourselves. In other words, computers are becoming full social actors that need to interact with people as seamlessly as possible. The key to development of computers as such social actors is to design human–computer interaction (HCI) that is human centered, built for humans based on human behavior models [1], [2]. In other words, HCI designs should focus on the human portion of the HCI context rather than on the computer portion, as was the case in classic HCI designs such as direct manipulation and delegation. They should transcend the traditional keyboard and mouse to include natural

journal homepage: www.elsevier.com/locate/neucom

Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper, we propose a multi-modal methodology based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier. Finally, the multi-modal cues are included in a sequential classifier. Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier. Moreover, the sequential methodology shows to significantly outperform the results obtained by an Adaboost classifier. 1.

Abstract—This paper describes a software framework we designed and implemented for the development and research in the area of multimodal human-computer interface. The proposed framework is based on publish / subscribe architecture, which allows developers and researchers to conveniently configure, test and expand their system in a modular and incremental manner. In order to achieve reliable and efficient data transport between modules while still providing a high degree of system flexibility, the framework uses a shared-memory based data transport protocol for message delivery together with a TCP based system management protocol to maintain the integrity of system structure at runtime. The framework is delivered as a communication middleware, providing a basic system manager and well-documented, easy-to-use and open source C++ SDKs supporting both module development and server extension. The experimental comparison between the proposed framework and other similar tools available to the community indicates that our framework greatly outperforms the others in terms of average message latency, maximum data throughput and CPU consumption level, especially in heavy workload scenarios. To demonstrate the performance of our framework in real world applications, we have built a demo system which is used to detect faces and facial feature points in real-time captured video. The result shows our framework is capable of delivering some tens of megabytes of data per second effectively and efficiently even under tight resource constraint.

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‘Summer Guests ’ (Dutch television program in which well known people are interviewed, using television fragments, chosen by themselves, about what they find important in their life): Actress about a fragment of a documentary she wants to show: “It’s so emotional, it’s so..”. Interviewer: “But first tell us what it’s about.”

We present a multilevel system architecture for intelligent environments equipped with omnivideo arrays. In order to gain unobtrusive human awareness, real-time 3D human tracking as well as robust video-based face detection and tracking and face recognition algorithms are needed. We first propose a multiprimitive face detection and tracking loop to crop face videos as the front end of our face recognition algorithm. Both skin-tone and elliptical detections are used for robust face searching, and viewbased face classification is applied to the candidates before updating the Kalman filters for face tracking. For video-based face recognition, we propose three decision rules on the facial video segments. The majority rule and discrete HMM (DHMM) rule accumulate single-frame face recognition results, while continuous density HMM (CDHMM) works directly with the PCA facial features of the video segment for accumulated maximum likelihood (ML) decision. The experiments demonstrate the robustness of the proposed face detection and tracking scheme and the three streaming face recognition schemes with 99 % accuracy of the CDHMM rule. We then experiment on the system interactions with single person and group people by the integrated layers of activity awareness. We also discuss the speech-aided incremental learning of new faces.

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