Human Gait Gender Classification using 2D Discrete Wavelet Transforms Energy

Human Gait as the recognition object is the famous biometrics system recently. Many researchers had focused this issue to consider for a new recognition system. One of the important advantage in this recognition compare to other is it does not require observed subject’s attention and assistance. There are many human gait datasets created within the last 10 years. Some databases that widely used are University Of South Florida (USF) Gait Dataset, Chinese Academy of Sciences (CASIA) Gait Dataset, and Southampton University (SOTON) Gait Dataset. This paper classifies human gender using the energy of 2DDiscrete Wavelet Transform in CASIA Gait Database. By using Backpropagation, the classification result is 92,9% accuracy.