Sébastien Marcel

A comparative study of two state-of-the-art sequence processing techniques for hand gesture recognition

In this paper, we address the problem of the recognition of isolated, complex, dynamic hand gestures. The goal of this paper is to provide an empirical comparison of two state-of-the-art techniques for temporal event modeling combined with specific features on two different databases. The models proposed are the Hidden Markov Model (HMM) and Input/Output Hidden Markov Model (IOHMM), implemented within the framework of an open source machine learning library (www.torch.ch). There are very few hand gesture databases available to the research community; consequently, most of the algorithms and features proposed for hand gesture recognition are not evaluated on common data. We thus propose to use two publicly available databases for our comparison of hand gesture recognition techniques. The first database contains both one- and two-handed gestures, and the second only two-handed gestures.

Audio-Visual processing in meetings: seven questions and current AMI answers

The project Augmented Multi-party Interaction (AMI) is concerned with the development of meeting browsers and remote meeting assistants for instrumented meeting rooms – and the required component technologies R&D themes: group dynamics, audio, visual, and multimodal processing, content abstraction, and human-computer interaction. The audio-visual processing workpackage within AMI addresses the automatic recognition from audio, video, and combined audio-video streams, that have been recorded during meetings. In this article we describe the progress that has been made in the first two years of the project. We show how the large problem of audio-visual processing in meetings can be split into seven questions, like “Who is acting during the meeting?”. We then show which algorithms and methods have been developed and evaluated for the automatic answering of these questions.