Authors:
Raúl Martín- Félez
;
Javier Ortells
;
Ramón A. Mollineda
and
J. Salvador Sánchez
Affiliation:
Universitat Jaume I, Spain
Keyword(s):
Gender classification, Gait, ANOVA, Feature selection.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Understanding
;
Pattern Recognition
;
Software Engineering
;
Video Analysis
Abstract:
Apart from human recognition, gait has lately become a promising biometric feature also useful for prediction of gender. One of the most popular methods to represent gait is the well-known Gait Energy Image (GEI), which conducts to a high-dimensional Euclidean space where many features are irrelevant. In this paper, the problem of selecting the most relevant GEI features for gender classification is addressed. In particular, an ANOVA-based algorithm is used to measure the discriminative power of each GEI pixel. Then, a binary mask is built from the few most significant pixels in order to project a given GEI onto a reduced feature pattern. Experiments over two large gait databases show that this method leads to similar recognition rates to those of using the complete GEI, but with a drastic dimensionality reduction. As a result, a much more efficient gender classification model regarding both computing time and storage requirements is obtained.