Human Gender Recognition with Upper Body Gait Kinematics

Human gender recognition has captured the attention of researchers particularly in computer vision and biometric arena. These investigations based on computer vision or image processing have highlighted applications in security systems, medical applications etc. This work is primarily aimed at investigating a possible characteristic difference in upper body movement kinematics between males and females and, in the affirmative, if that kinematic information alone is sufficient to distinguish each cohort from the other. We use a Microsoft Kinect© to capture the human upper body gait kinematics to uncover gender based kinematic variations. Two groups of healthy volunteers (18 females, 16 males) were requested to walk along a linear pathway in front of the camera. Upper body movement kinematics were extracted from the male and female cohorts during walking. Principal component analysis (PCA) was employed to substantiate the differences between the two cohorts in terms of kinematic information. Finally, we use k-means clustering to classify and evaluate the performance of the classification system. Despite of the limitation of the dataset, e.g., the limited range of the Kinect© camera, the accuracy of the proposed approach reached up to 94%, indicating that upper body joint movements possess significant information content on human gender based features.