Recognizing individuals by their gait is a new biometric methodology, which employs dynamic features derived from tracking gait. Instead of the image processing techniques used in most existing studies, our previous study initialized the work of investigating gait recognition in terms of biomechanics. The experimental results showed that the angles and forces of the lower limb joints were reliable features for recognition of individuals, which can provide us with a considerable amount of information in the field of computer science and thus help in developing a more efficient recognition method, which is also more computationally efficient than current image processing methods. Encouraged by the early results, in this study, we proposed a people recognition method based on plantar pressure patterns, which can be used in a concealed manner. We hoped to prove the feasibility of using foot pressure for individual recognition. Two different plantar pressure parameter measurement schemes are discussed: (1) the characteristic parameters and (2) the pressure values of each sensor cell in each frame. The self-organizing map (SOM) neuron network algorithm was used in both schemes for data classification. In order to improve the recognition rate, a support vector machine (SVM) was used as the data classification algorithm for the all-sensor-values method. High recognition rates were achieved with the second method, i.e., using all the sensor cell values of the foot pressure pattern during walking, regardless of the algorithm used. It is suggested that the foot pressure distribution of gait is a suitable feature for gait recognition. Both SOM and SVM can be feasible classifiers for foot pressure-based features.
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