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In the traditional login systems, we use the username and the password to identify the legalities of users. It is a simple and convenient way to identify, but passwords could be stolen or copied by someone who tries to invade the system illegally. Adding one protective mechanism to identify users, the way of biometrics are brought out, such as keystroke dynamics, fingerprints, DNA, retinas and so on that are unique characteristics of each individuals, it could be more effective in preventing trespassing. This thesis uses keystroke biometrics as research aspects of user authentication. The advantages of this system are low-cost and high security to identify users using keyboard to calculate the time of keystrokes. In this thesis, we use statistical way to examine the researches and experiments. Chosen length of the username and password are greater than or equal to 9 characters, and learning sample sizes are 20 and adapting the sample adaptation mechanism, the results show that we achieved by False Acceptance Rate of 0.85%, False Rejection Rate of 1.51% and Average False Rate of 1.18%; all reach the high levels of safeties.

In login systems, a user is asked to enter his correct account and password in order to be allowed to enter to the system. The safety of systems is at the risk of leaking out the information, hence, the single mechanism of identity verification has not filled the bill at present. We study the personal typing behavior to get one¡¦s own specific features. In our thesis , we compare three methods and anlysis the advantages and shortcomings of those three. First one is to sort the twenty study data, and distribute the weights into the proper region. If the total weights is less than the threshold then this test data will be accepted, otherwise, it will be rejected. The second and third method are similar. Both of them are trying to rescale the data. The spirit of them is that the typing rate of a person will be faster when they type frequently and will be sloer when they are out of practice. However the relative positions of those keys, the lengths of ons¡¦s fingers, and the time that people making pauses in reading unpunctuated are unique. Those factors can be one¡¦s typing rhythm. There are twenty two individuals involved in this experiment. Each one choose his own proficient account and password to type and set up his typing model. The imposters are randomly choose legal user to imitate.

This master thesis deals with recognition of a person by comparing symptom sets extracted from images of the retinal vessels pattern. The first part includes the insight into biometric issues, the punctual analysis of human identification using retina images, and especially the literature research of methods of extraction and comparison. In the practical part there were realized algorithms for human identification with the method of nearest neighbor search (NS), translation, template matching (TM) and extended NS and TM including more symptoms, for which MATLAB program was used. The thesis includes testing of suggested programs on the biometric database of symptomatic vectors with the following evaluation.