Human recognition through walking styles by multiwavelet transform

Human recognition through walking styles is among the newest of biometric methods. By using this biometric, individuals can be identified, distantly, even at low visibility. Our aim is to provide such ability for a computer system. In other words, we intend to extract appropriate features through processing video images that can reflect individuals' identity. In order to set up such a system, we have used Fourier, Wavelet, and Multi-wavelet transforms. Using images from the USF dataset version 1.7, the results obtained indicate that SA4 Multi-wavelet transforms prove more efficient in extracting suitable features than Fourier and wavelet transforms, and combined with one-versus-one Support Vector Machine, they can provide a 85.7 % recognition accuracy rate. Our proposed method shows higher accuracy and precision compared to other frequency based methods.