Abstract
This paper addresses the problem of recognizing a human body posture from a cloud of 3D points acquired by a Human body scanner. Motivated by finding a representation that embodies a high discrimination power between posture classes, a new type of features is suggested, namely, the wavelet transform coefficients (WTC) of the 3D data points distribution projected on the space of the spherical harmonics. A Feature selection technique is developed to find the features with high discriminatory power. Integrated within a Bayesian classification framework and compared with other standard features, the WTC showed great capabilities in discriminating between close postures. The qualities of the WTC features were also reflected on the experiment results carried out with artificially generated postures, where the WTC got the best classification rate. To the best of our knowledge, this work appears to be the first to treat the posture recognition in the three-dimensional case and to suggest WTC as features for 3D shape.