Raúl Martín- Félez, Javier Ortells, Ramón A. Mollineda, J. Salvador Sánchez


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.


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Paper Citation

in Harvard Style

Martín- Félez R., Ortells J., A. Mollineda R. and Salvador Sánchez J. (2012). EFFICIENT GAIT-BASED GENDER CLASSIFICATION THROUGH FEATURE SELECTION . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 419-424. DOI: 10.5220/0003774404190424

in Bibtex Style

author={Raúl Martín- Félez and Javier Ortells and Ramón A. Mollineda and J. Salvador Sánchez},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},

in EndNote Style

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
SN - 978-989-8425-99-7
AU - Martín- Félez R.
AU - Ortells J.
AU - A. Mollineda R.
AU - Salvador Sánchez J.
PY - 2012
SP - 419
EP - 424
DO - 10.5220/0003774404190424