Joint Swing Energy for Skeleton-Based Gender Classification

Human gender classification in digital image content has received considerable attention from researchers for various applications, such as demographic research, video surveillance systems, and forensic science. In this study, we investigate three-dimensional (3D) human skeleton-based gender classification using a novel gait feature called joint swing energy (JSE). JSE is a kinematic gait feature that represents how distant a model skeleton's body joints are from anatomical planes while walking. However, anatomical planes are conventionally obtained from single static poses rather than dynamic motion. Therefore, in this study, we further investigate a novel method for obtaining transverse, frontal, and median planes from a 3D gait sequence. Using these planes, each joint's movement can be represented by coordinates centered on a human body rather than 3D Cartesian coordinates. Using the proposed methods, we extract JSEs of body joints from 3D gait sequences. We show that JSEs are different between walking men and women and propose the use of JSEs for machine classification of human gender. To demonstrate the effectiveness of the proposed JSE model on the gender classification task, we evaluate the performance of machine learning algorithms trained using JSE on four publicly available datasets, referred to as Datasets A, B, C, and D. Dataset A includes gait sequences from 164 persons between the ages of 17 and 45. Dataset B contains gait sequences from 104 persons between the ages of 17 and 36. In Dataset C, there are gait sequences for 30 persons between the ages of 23 and 55. In Dataset D, gait sequences for 30 persons between the ages of 21 and 55 are contained. The evaluation results demonstrate that the proposed technique achieves the highest classification accuracy and outperforms existing techniques for all datasets. These results suggest that human gender can be classified by JSEs extracted from the 3D gait sequence.