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.