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Human-computer interaction has been widely used in the areas of games, health-care, entertainment, pattern recognition, and identity authentication. Due to the booming emerging of technological progress, the limitation of using mouse or keyboard to control 3C device is no longer a must. With the fast development of human-computer interaction recently, it is now possible to use air-handwriting, gesture, and so on to control 3C devices in public areas or at homes.
Traditional identity authentication usually relies heavily on the using of passwords or cryptographic keys. However, it is not only needing to remember the password or keeping the cryptographic key all the time but also easily losing passwords or keys and thereby suffering from password theft. On the contrary, using the unique features of air-handwriting signatures for identity authentication can resolve these problems. Furthermore, it is also cheaper than other biological authentication systems which must rely on expensive devices to acquire features such as face, voiceprint, fingerprints, iris and so forth.
In this thesis, we use Leap Motion to obtain the signatures of users and find the location of turning points in the signature trajectory as the basis for stroke cutting. Then, calculate the Shape Context of accumulate strokes, velocity and curvature of turning points as the features. In features matching, Dynamic Time Warping (DTW) is employed to calculate the distance between two signatures. In our experiments, we try to figure out the experimental results in each case such as combinations of different thresholds and different matching distances no matter whether performing data analysis or data clustering in advance or not to analyze the performance of our proposed method. Experimental results demonstrate the excel performance of our proposed system in identity authentication. | en_US |