Gender recognition of human behaviors using neural ensembles

J. Ryu, S. B. Cho

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Number of pages6
Publication statusPublished - 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: 2001 Jul 152001 Jul 19


OtherInternational Joint Conference on Neural Networks (IJCNN'01)
Country/TerritoryUnited States
CityWashington, DC

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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