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 footpressure image that can be used as a reliable biometric scale for human verification. foot pressure colorimages of center for biometrics and security research (cbsr) dataset from 45 men and five womenwere used in this study. owing to the properties of this dataset, an index of foot pressure in addition toexternal feature and contourlet coefficient of images was extracted. a multilayer perceptron (mlp) wasutilized for verification of subjects (it is a common practice to explain more about the training and testdataset). to validate the algorithm performance, results were obtained using a 5-fold cross validationapproach.the results indicated accuracy of 99.14 ± 0.65 and equal error rate (eer) of 0.02. theseresults demonstrated the reliability of proposed neural network in human verification application. hence,it can be utilized in other verification systems.