Audio-visual recognition system in compression domain

Yee Wan Wong; Kah Phooi Seng; Li-Minn Ang

doi:10.1109/TCSVT.2011.2129670

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Audio-visual recognition system in compression domain

Journal article

Peer reviewed

Audio-visual recognition system in compression domain

Yee Wan Wong, Kah Phooi Seng and Li-Minn Ang

IEEE Transactions on Circuits and Systems for Video Technology, Vol.21(5), pp.637-646

2011

DOI: https://doi.org/10.1109/TCSVT.2011.2129670

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Abstract

audio-visual recognition

computational complexity

face localization

face segmentation

video-based face recognition

wavelet transform

This paper presents a highly efficient audio-visual recognition system in compression domain. For face recognition systems, the multiband feature fusion method selects the wavelet subbands that are invariant to illumination and facial expression variations. These subbands will be extracted directly from the inverse quantization in the compression system. By taking the inverse quantized wavelet coefficient of the video as the input, the inverse wavelet transform which corresponds to image reconstruction is omitted. As a result, the computational complexity of the conventional video-based face recognition system is reduced. We also present a set of new face localization methods to localize the facial wavelet coefficients from the wavelet subband image. The dual optimal multiband feature fusion method is then used to fuse the two set of wavelet coefficients and generate the visual scores. Experimental results show that with low computational complexity, the proposed system achieves high recognition accuracy in UNMC-VIER, CUAVE, and XM2VTS audio-visual database.

Details

Title: Audio-visual recognition system in compression domain
Authors: Yee Wan Wong (Author) - Taylor's University
Kah Phooi Seng (Author) - University of Nottingham Malaysia Campus
Li-Minn Ang (Author) - University of Nottingham Malaysia Campus
Publication details: IEEE Transactions on Circuits and Systems for Video Technology, Vol.21(5), pp.637-646
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2011
DOI: 10.1109/TCSVT.2011.2129670
ISSN: 1051-8215; 1558-2205; 1051-8215
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99513888802621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Engineering, Electrical & Electronic