People recognition with foot pressure based on neural network

To find the suspect in forensic cases, a unique feature of shoe prints can be used. It is effective to distinguish people with shape of shoes and gait features demonstrated by many studies. To address the limitations stemming from relying solely on a single feature, such as complex equipment requirements and susceptibility to interference, this study explores a people identification system that leverages a BP neural network while integrating both shape and plantar pressure features. With 3 different approaches of data processing compared, an accuracy of 89% with no noises and 74%with10% gaussian noise. The result demonstrated that the system is effectiveness on people identification.