Human recognition based on gait is a new biometric method that considers both spatial and temporal features which tracks the gait of a human being from a distance. The most widely used techniques for gait recognition at present can be divided into two categories: the model-free method and the model-based method. Both these methods process images of a moving body. Although some effort has been devoted to gait recognition, a high identification rate and low computational cost still need to be achieved. In this study, we investigated gait recognition in terms of biomechanics, which is often overlooked in the fields of computer science and electronics. We present a method for people recognition by using both the three-dimensional parameters for lower limb joints, i.e., kinematic and kinetic parameters. Dynamic data are acquired by tracking marker positions using a motion capture system with force plates. Although a marker-free and cable-free biometric system is more applicable for security purposes, acquiring data by marker tracking is one of the most precise methods currently available. The results obtained from marker tracking can clarify the relationship between the recognition rate and dynamic data of normal walking. In this study, 350 trials were conducted for 10 subjects. For each subject, 20 trials were conducted for training and the remaining 15 were for testing. A self-organizing map (SOM) neural network was employed for data classification. The importance of a specific variable in each dimension for each joint is discussed in detail to investigate the major factors that cause the differences in human gait according to a biomechanical approach. Experimental results showed that gait is a reliable feature for individual identification since a high recognition rate can be achieved by choosing appropriate joints or dynamic parameters in some dimensions of a gait. Kinematic variables in the frontal and transverse planes of the knee or hip joints are recommended due to their higher recognition rates. These findings can be applied to both biomedicine and computer science.
- Individual identification
- Neural network