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tically reduced ranging from approximately 1100
features with χ = 10% to about 100 features with
χ = 1%. It produces a strong improvement in
terms of time and storage costs.
• Gmean results are more reliable than those of Acc
because both databases have an imbalanced num-
ber of gait sequences corresponding to each gen-
der. For example, for the particular case of SO-
TON and 1NN classifier, a relatively high accu-
racy of about 80% is obtained with the original
GEI method, but it hides an inadmissible 40% of
success on the women class with an almost per-
fect classification rate for the men class (98%). In
this case, Gmean better represents this biased be-
haviour with a low value of about 60%.
• The proposed method obtains Gmean and Acc val-
ues similar to those of the original GEI, but with a
drastic dimensionality reduction. For SVM clas-
sifier, the best balanced trade-off between perfor-
mance and number of features might correspond
to the 3% Mask. However, for 1NN classifier, the
best solution is that with the highest dimensional-
ity reduction (1% Mask). This classifier shows a
tendency to improve its results as the number of
features decreases. In fact, the worst results cor-
respond to the original GEI, i.e., with all features.
A possible reason is that the number of samples
is too low in comparison with the number of fea-
tures, what produces a very spread feature space.
By considering both performance and number of
features, the best solution is probably that in which
1NN classifier is used with the 1% Mask, since differ-
ences with the best SVM results are not significant.
5 CONCLUSIONS
In this paper, an ANOVA-based algorithm has been
used to select the most relevant GEI features for gen-
der classification. The experiments carried out on two
large databases with a SVM and a 1NN classifiers
have shown that a similar performance to that of the
original GEI can be achieved by using only its most
discriminative information, what leads to an impor-
tant reduction in computing time and storage require-
ments. In particular, the 1NN approach has obtained
the highest success rates (comparable or better than
those of SVM) with the lowest number of features.
With respect to future work, a more comprehen-
sive study including other feature selection/extraction
methods should be addressed. In addition, the param-
eter χ should be automatically estimated by using a
validation set, since its value might depend on the sin-
gularities of the gallery set. In SOTON, the higher
imbalanced ratio produces worse results, thus some
techniques to deal with imbalance should be applied
in order to improve the overall Gmean result.
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