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Compressive sensing for gait recognition

Sivapalan, Sabesan, Rana, Rajib.K, Chen, Daniel, Sridharan, Sridha, Denman, Simon, & Fookes, Clinton B. (2011) Compressive sensing for gait recognition. In Proceedings of Digital Image Computing : Techniques and Applications (DICTA2011), IEEE, Sheraton Noosa Resort & Spa, Sunshine Coast, QLD. (In Press)

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Abstract

Compressive Sensing (CS) is a popular signal processing technique, that can exactly reconstruct a signal given a small number of random projections of the original signal, provided that the signal is sufficiently sparse. We demonstrate the applicability of CS in the field of gait recognition as a very effective dimensionality reduction technique, using the gait energy image (GEI) as the feature extraction process. We compare the CS based approach to the principal component analysis (PCA) and show that the proposed method outperforms this baseline, particularly under situations where there are appearance changes in the subject. Applying CS to the gait features also avoids the need to train the models, by using a generalised random projection.

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ID Code: 46383
Item Type: Conference Paper
Keywords: Compressive sensing, Sparse learning, Principal component analysis, Gait recogntion, Gait energy image
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2011 [please consult the authors]
Deposited On: 12 Oct 2011 09:39
Last Modified: 13 Oct 2011 20:54

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