Real-Time Dynamic and Multi-View Gait-Based Gender Classification Using Lower-Body Joints

Gender classification based on gait is a challenging problem because humans may walk in different directions at different speeds and with varying gait patterns. The majority of investigations in the literature relied on gender-specific joints, whereas the comparison of the lower-body joints in the literature received little attention. When considering the lower-body joints, it is important to identify the gender of a person based on his or her walking style using the Kinect Sensor. In this paper, a logistic-regression-based model for gender classification using lower-body joints is proposed. The proposed approach is divided into several parts, including feature extraction, gait feature selection, and human gender classification. Different joints’ (3-dimensional) features were extracted using the Kinect Sensor. To select a significant joint, a variety of statistical techniques were used, including Cronbach’s alpha, correlation, T-test, and ANOVA techniques. The average result from the Coronbach’s alpha approach was 99.74%, which shows the reliability of the lower-body joints in gender classification. Similarly, the correlation data show a significant difference between the joints of males and females during gait. As the p-value for each of the lower-body joints is zero and less than 1%, the T-test and ANOVA techniques demonstrated that all nine joints are statistically significant for gender classification. Finally, the binary logistic regression model was implemented to classify the gender based on the selected features. The experiments in a real situation involved one hundred and twenty (120) individuals. The suggested method correctly classified gender using 3D data captured from lower-body joints in real-time using the Kinect Sensor with 98.3% accuracy. The proposed method outperformed the existing image-based gender classification systems