Biometric Authentication Based On Plantar Pressure And Ground Reaction Force

The rapid development of telecommunication technology and internet has fundamentally changed people's lifestyle, and the society proposes higher demand on the veracity, security and practicability of personal authentication, therefore, biometric authentication technology has drawn wide attention around the world.Plantar pressure and ground reaction force (GRF) based biometric authentication method is a novel personal identification technology. Compared with other methods, it has distinct advantages:first, the measurement of the information does not need the cooperation of the targeted subjects, for it can collect the information without privacy intrusion; second, plantar pressure and ground reaction force are determined by innate and postnatal factors:innate factors include the bone structure of human foot while postnatal factors refer to the environment, personal habits and so on. Therefore, these characteristics are distinctive, relatively stable and hard to disguise, making them perfect for using in biometric authentication.Research on biometric authentication based on plantar pressure and GRF is just started, and there's little information on it, thus this paper will first introduce some basic knowledge about plantar pressure and GRF; then it will deeply study on how to accomplish personal identification with them, the main works of the dissertation are as follows:The first part gives brief introduction of the characteristics'physiological feature, type, model and application, while focusing on the steps and key problems in biometric authentication using GRF.Based on the analysis of the principle and deficiency of the popular information acquisition instruments of plantar pressure and ground reaction force, a platform combining pressure platform and force platform was designed and developed, and it can measure plantar pressure and ground reaction force at the same time. Using this platform, information was collected from different people, and a database was established. Considering the signal's feature, a suitable method was used to denoising the original information, while maintaining some distinctive features. The repeatability of the signal for the same person and the uniqueness of the signal for different people were tested, proving that the signal is suitable to be used in biometric authentication.After that the paper discussed how to extract effective features from the denoised signals, which contain large amounts of useful features and how to extract them is very important for improving the efficiency of the authentication. Analyzing the principle of some popular feature extraction method and considering the characteristics of the GRF signals, wavelet packet decomposition (WPD), which can yield a large number of different decompositions and improve the time-frequency resolution, was used for this purpose. Experiments were carried to analyse subjects in the database, during which the original signals were decomposed to different frequencies, and the WP coefficients, which can best represent the characteristics of the signals were chosen as the original feature representation.Feature selection and classification method were discussed in the next chapter. Feature selection, which can reduce the dimensionality of the feature and provide better and more effective model, is a key step in authentication, for the feature subsets have a decisive role in determining the classification efficiency. A hybrid feature selection method was proposed:in the first step, a fuzzy set based feature selection criterion was used to drop unrelated features while genetic algorithm (GA) or ant colony optimization (ACO) was used in the second step to pick up pivotal ones. Support vector machine (SVM) was used for classification during the second selection process, for it can effectively avoid local optimum, guarantee best resolution and is very suitable in small sample classification problems. To show the availability of the algorithms, experiments were made to analyse the database, the classification efficiency and feature reconstruction of the pure one-step feature selection method and the proposed algorithms were compared, as well as the effect of using GA and ACO in the second selection process.Based on the above researches, the last part of the paper introduced the algorithm of biometric authentication based on multi-cycle GRF and a biometric authentication system was established. The system structure and proposed algorithm were explained in detail, and a database was established. The verification and practicability of the system was proved with authentication test, and signal reconstruction was carried out to discuss the essence of the selected feature.