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Automatic Image Collection of Objects with Similar Function by Learning Human Grasping Forms Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Audio-Visual User Identification in HCI Scenarios

Authors : Markus Kächele, Sascha Meudt, Andrej Schwarz, Friedhelm Schwenker

Published in: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Publisher: Springer International Publishing

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Abstract

Modern computing systems are usually equipped with various input devices such as microphones or cameras, and hence the user of such a system can easily be identified. User identification is important in many human computer interaction (HCI) scenarios, such as speech recognition, activity recognition, transcription of meeting room data or affective computing. Here personalized models may significantly improve the performance of the overall recognition system. This paper deals with audio-visual user identification. The main processing steps are segmentation of the relevant parts from video and audio streams, extraction of meaningful features and construction of the overall classifier and fusion architectures. The proposed system has been evaluated on the MOBIO dataset, a benchmark database consisting of real-world recordings collected from mobile devices, e.g. cell-phones. Recognition rates of up to 92 % could be achieved for the proposed audio-visual classifier system.

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Metadata
Title
Audio-Visual User Identification in HCI Scenarios
Authors
Markus Kächele
Sascha Meudt
Andrej Schwarz
Friedhelm Schwenker
Copyright Year
2015
Book
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Print ISBN: 978-3-319-14898-4

Electronic ISBN: 978-3-319-14899-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-14899-1_11

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