A discriminative 3D wavelet-based descriptors: Application to the recognition of human body postures

This paper deals with the recognition of human body postures from a cloud of 3D points acquired by a human body scanner. Motivated by finding a representation that embodies a high power of discrimination between posture classes, a new type of 3D shape descriptors is suggested, namely wavelet transform coefficients (WC). These features can be seen as an extension to 3D of the 2D wavelet shape descriptors developed by (Shen, D., Ip, H.H.S., 1999. Pattern Recognition, 32, 151-165). The WC is compared with other 3D shape descriptors, within a Bayesian classification framework. Experiments with real scan data show that the WC outperforms other standard 3D shape descriptors in terms of discrimination power and classification rate.