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Data-independent versus data-dependent dimension reduction for gait-based gender classification
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Paper Abstract

In most pattern recognition applications, the object of interest is represented by a very high dimensional data-vector. High dimensionality of modeling vectors poses serious challenges related to the efficiency of retrieval, analysis and classifying the pattern of interest. The Curse of Dimension is a general reference to these challenges and commonly addressed by Dimension Reduction (DR) techniques. The most commonly used DR schemes are data-dependent like Principal Component Analysis (PCA). However, we may expect over-fitting and biasness of the adaptive models to the training sets as consequences of low sample density ratio to dimension. Therefore, data-independent DR schemes such as Random Projections (RP) are more desirable. In this paper, we investigate and test the performance of differently constructed overcomplete Hadamard-based mxn (m<<n) sub-matrices using Walsh-Paley (WP) matrices as a DR scheme for Gait-based Gender Classification (GBGC). In particular, we shall demonstrate that these Hadamard-based RPs perform as well as, if not better, PCA and Gaussian-based RPs. Moreover, we shall show that Walsh-Paley Structured Matrices (WPSM) perform better than Walsh-Paley Random Matrices (WPRM).

Paper Details

Date Published: 14 May 2018
PDF: 8 pages
Proc. SPIE 10668, Mobile Multimedia/Image Processing, Security, and Applications 2018, 106680I (14 May 2018); doi: 10.1117/12.2310154
Show Author Affiliations
Tahir Hassan, The Univ. of Buckingham (United Kingdom)
Azhin Sabir, Koya Univ. (Iraq)
Sabah Jassim, The Univ. of Buckingham (United Kingdom)

Published in SPIE Proceedings Vol. 10668:
Mobile Multimedia/Image Processing, Security, and Applications 2018
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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