A Review on Feature Identification and Extraction of Cumulative Foot Pressure Images
The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Biometrics is a dynamic industry resulting in sustained evolution of numerous new technologies. The biometric system is needed in many key areas such as banking, airport, criminal cases, security purpose etc. Gait is an important biometric technology to recognize a human by the manner, they walk. Biometric identification based on body movements as gait recognition has gained new motivation over the past few years due to the launch of low cost depth cameras. Cumulative foot pressure image is a 2-D cumulative ground reaction force during one gait cycle. Although it contains pressure spatial distribution information and pressure temporal distribution information, it suffers from several problems including different shoes and noise, when putting it into practice as a new biometric for pedestrian identification. This paper aims to overview a pedestrian recognition system which is invariant to three different shoes and slight local shape change which is based on feature representation of cumulative foot pressure images. This review also discusses established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It also reviews that every person produces a unique trajectory of underfoot pressures while walking and that CNNs can learn the distinctive features of these trajectories. By applying a pretrained CNN (transfer learning), a couple of strides seem enough to learn and identify new gaits.