Human Gait Gender Classification using 3D Discrete Wavelet Transform Feature Extraction

Feature extraction for gait recognition has been created widely. The ancestor for this task is divided into two parts, model based and free-model based. Model-based approaches obtain a set of static or dynamic skeleton parameters via modeling or tracking body components such as limbs, legs, arms and thighs. Model-free approaches focus on shapes of silhouettes or the entire movement of physical bodies. Model-free approaches are insensitive to the quality of silhouettes. Its advantage is a low computational costs comparing to model-based approaches. However, they are usually not robust to viewpoints and scale. Imaging technology also developed quickly this decades. Motion capture (mocap) device integrated with motion sensor has an expensive price and can only be owned by big animation studio. Fortunately now already existed Kinect camera equipped with depth sensor image in the market with very low price compare to any mocap device. Of course the accuracy not as good as the expensive one, but using some preprocessing method we can remove the jittery and noisy in the 3D skeleton points. Our proposed method is to analyze the effectiveness of 3D skeleton feature extraction using 3D Discrete Wavelet Transforms (3D DWT). We use Kinect Camera to get the depth data. We use Ipisoft mocap software to extract 3d skeleton model from Kinect video. From the experimental results shows 83.75% correctly classified instances using SVM.