In this paper we present a new perspective into developing technologies for enriching social presence among remote interaction partners. Inspired by the abilities and limitations faced by people who are disabled during their everyday social interactions, we propose novel portable and wearable technologies that could potentially enrich remote interactions even in audio and video deprived settings. We describe the important challenges faced by people who are disabled during everyday dyadic and group social interactions and correlate them to the challenges faced by participants in remote interactions. With a case study of visually impaired individuals, we demonstrate how assistive technologies developed for social assistance of people who are disabled can help in increasing the social situational awareness, and hence social presence, of remote interaction partners.

This paper describes the iCare Interaction Assistant, an assistive device for helping the individuals who are visually impaired during social interactions. The research presented here addresses the problems encountered in implementing real-time face recognition algorithms on a wearable device. Face recognition is the initial step towards building a comprehensive social interaction assistant that will identify and interpret facial expressions, emotions and gestures. Experiments conducted for selecting a face recognition algorithm that works despite changes in facial pose and illumination angle are reported. Performance details of the face recognition algorithms tested on the device are presented along with the overall performance of the system. The specifics of the hardware components used in the wearable device are mentioned and the block diagram of the wearable system is explained in detail.

This presentation demonstrates a working prototype of the iCare Interaction Assistant, a wearable assistive device based on research aimed at facilitating the social interactions of people who are blind or visually impaired. Using a tiny unobtrusive camera mounted inside the nose bridge of a pair of eyeglasses, this prototype is able to learn and recognize faces at a distances up to 10 feet, thus allowing the user to initiate conversations with persons in their vicinity, without waiting for others to approach them. Ongoing work is aimed at facilitating the subsequent verbal interaction by recognizing and interpreting non-verbal communication, including eye contact, facial expressions, emotions, and gestures.

In this paper, we present a methodology for precisely comparing the robustness of face recognition algorithms with respect to changes in pose angle and illumination angle. For this study, we have chosen four widely-used algorithms: two subspace analysis methods (principle component analysis (PCA) and linear discriminant analysis (LDA)) and two probabilistic learning methods (hidden Markov models (HMM) and Bayesian intra-personal classifier (BIC)). We compare the recognition robustness of these algorithms using a novel database (FacePix) that captures face images with a wide range of pose angles and illumination angles. We propose a method for deriving a robustness measure for each of these algorithms, with respect to pose and illumination angle changes. The results of this comparison indicate that the subspace methods perform more robustly than the probabilistic learning methods in the presence of pose and illumination angle changes.

This paper proposes a two-step methodology for improving the discriminatory power of linear discriminant analysis (LDA) for video-based human face recognition. Results indicate that, under real-world video capture conditions, face images extracted from a video sequence have enough 3D rotations, illumination changes and background variations to reduce the discriminatory power of an LDA classifier. The proposed method involves deriving an LDA subspace from carefully selected subsets of face images that fall within a narrow range of pose angles, and then growing the classification regions in the LDA subspace using face images with a wider range of pose angle changes, illumination changes, and background variations. Polynomial support vector machines (SVM) are shown to provide better recognition rates by defining the boundaries between clusters that represent the faces of different subjects. Results show that there is an improvement in the recognition rate when the LDA subspace is derived with this methodology than when it is derived with a set of face images with a widely divergent set pose angles, illumination variations, and backgrounds.