Gender Recognition of Human Behaviors Using Neural Ensembles
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ABSTRACT:
In this paper, we have developed two ensembles of neural
network classifiers in order to recognize actors’ gender
from their biological movements. One is the ensemble of
modular MLPs (experts), the other is the ensemble of
modular MLPs and an inductive decision tree which
combines the output of experts. The human movement
database consists of 13 males’ and 13 females’ movements,
and contains 10 repetitions of knocking, waving and lifting
movements both in neutral and angry style. Features have
been extracted with 4 different representations such as the
2D and 3D velocities and positions, recorded from 6 point
lights attached on body. We have compared the results of
ensembles to the regular classifiers such as MLP, decision
tree, self-organizing map and support vector machine.
Furthermore, the discriminability and efficiency have been
calculated for the comparison with the human performance
that has been obtained with the same experiment. Our
experimental results indicate that the ensemble models are
superior to the conventional classifiers and human
participants.
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