Human Verification Using a Combination of Static and Dynamic Characteristics in Foot Pressure Images

Since gait is the mixture of many complex movements, each individual can define with a unique foot pressure image that can be used as a reliable biometric scale for human verification. foot pressure color images of center for biometrics and security research (cbsr) dataset from 45 men and 5 women were used in this study. owing to the properties of this dataset, an index of foot pressure in addition to external feature and contourlet coefficient of images was extracted. a multilayer perceptron (mlp) was utilized for verification of subjects (it is a common practice to explain more about the training and test dataset). to validate the algorithm performance, results were obtained using a 5-fold cross validation approach. the results indicated accuracy of 99.14 ± 0.65 and equal error rate (eer) of 0.02. these results demonstrated the reliability of proposed neural network in human verification application. hence, it can be utilized in other verification systems.