A Smart Mat for People Identification Based on BP Neural Network

A shoe print is a unique feature of each shoe, and is used in the forensic case to identify the suspect. Many kinds of research showed that it is effective in identifying people with shoe shape and gait features. Although the researches have achieved high accuracy in people identification, immunity to interference was generally poor and the devices they used were high-cost. This paper designed a low-cost hardware system based on a thin-film pressure sensor to collect plantar pressure data. The data were sampled and pre-processed by an MCU with internal ADC peripherals and transmitted to a PC synchronously. Then the data were enhanced and trained by a BP neural network with shoe shape and plantar pressure features combined. By comparing the 3 different pressure data accumulation methods, the system achieved a best accuracy of 89% without noise and 74% with about 10% noise pixels added. The result of the experiment demonstrated the effectiveness of this system.