Frontal view gait analysis of gender

The study aimed to investigate the potential of frontal view gait of human for gender recognition based on model based approach. Firstly, 128 features are extracted based on four parameters from the lower limb of human body specifically the left and right hip angles along with both left and right knee angles and these features are validated for gender recognition purpose. Next, statistical analysis and PSO are evaluated as feature selection in identifying the significant features amongst the original extracted gait features. Results attained with ANN as classifier proven that the original features extracted based on frontal view is capable to classify gender whilst PSO as subset selection showed promising accuracy rate with average of 85% for gender classification using the proposed front view gait technique.