Wavelet moments for recognizing human body posture from 3D scans

This paper addresses the problem of recognizing a human body (HB) posture from a cloud of 3D points acquired by a human body scanner It suggests the wavelet transform coefficients (WTC) as 3D shape descriptors of the HB posture. The WTC showed to have a high discrimination power between posture classes. Integrated within a Bayesian classification framework and compared with other standard moments, the WTC showed great capabilities in discriminating between close postures. The qualities of the WTC features were also reflected on its classification rate, ranked first when compared with other 3D features.