Human Sex Recognition Based on Dimensionality and Uncertainty of Gait Motion Capture Data

The paper proposes a method of human sex recognition using individual gait features extracted by measures describing the dimensionality and uncertainty of non-linear dynamical systems. The correlation dimension and sample entropy are computed for time series representing angles of skeletal body joints as well as whole-body orientation and translation. Two aggregation strategies for pose parameters are used averaging of Euler angles triplets and taking an angle of 3D rotation. In the baseline variant, the distinction between females and males is performed by thresholding the obtained measure values. Moreover, the supervised classification is carried out for the complex gait descriptors characterizing the movements of all bone segments. In the validation experiments, highly precise motion capture measurements containing data of 25 female and 30 male individuals are used. The obtained, at least promising, performance assessed by correct classification rate, the area under the receiver operating characteristic curve, and average precision, is higher than 89%, 96%, and 96%, respectively, and exceeds our expectations. Moreover, the classification accuracy based on a ranking of skeletal joints, as well as whole-body orientation and translation evaluating sex-discriminative traits incorporated in the movements of bone segments, is formed.