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.
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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|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:||11 Oct 2011 23:39|
|Last Modified:||24 Jun 2015 19:19|
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