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ORIGINAL ARTICLE
Year : 2016  |  Volume : 6  |  Issue : 4  |  Page : 224-230

Human verification using a combination of static and dynamic characteristics in foot pressure images


Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Correspondence Address:
Keivan Maghooli
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2228-7477.195089

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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.


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