Automatic Fingerprint Identification Algorithm

Accurate, secure and practical personal identification methods are highly required with the development of the social and economy. Biometrics based on the physiological or behavior traits identification provides a convenient and reliable scheme. Among the numerous biometrics, more attentions have been paid to the fingerprint identification technology due to its convenience, high accuracy and low cost. The technique of fingerprint identification has become one of the widest used biometric identification techniques. It has been widely used in electronic commerce,criminal identification, information safety etc.Automated Fingerprint Identification System is an integrated system which concentrate the photoelectric technology, image processing, computer and networking, database technology, pattern recognition technology. In the view of the operation of the APIS, based on the conclusion of the existing research results, I research the key algorithm in the fingerprint image pretreatment preprocessing, feature extraction, fingerprint classifier and feature matching algorithm of the fingerprint authentication system in this paper. Major research and the results are as follows:(1)The orientation is calculated by repetitious Gaussian filtering on fingerprint images through which noises can be removed well. According to the founded orientations, we rotate the original images divided by corresponding blocks. It is convenient to get the statistical frequencies by projecting the rotated blocks along axis Y. Fingerprint enhancement is processed through Gabor filter. And the processing time is reduced by using the average value of effective frequencies as the frequency of the whole image. A discriminant based on reliability is also adopted to improved the binary images. The algorithm presented is simple and effective, and easier to realized.(2) The existed minutiae extraction and classification algorithms. A tow-stage fingerprint classification algorithm based on the Support Vector Machine is presented. The SVM method is based on strict mathematical theory, find the optimal SRM hyperplane guided by the principle, displayed good generalization ability, the sufficiency uses SVM theory which the advantages take on the second category classification. The training sample have better promoting capacity.(3) A minutiae matching method combining using genetic algorithm is proposed. Using a modified points matching method based on genetic algorithm to find the best minutiae corresponding relations which having the most matching pairs and minimum matching error. Then reason the matching score based on defined the relations between the number of matching pairs and the matching error.Finally, according to summary of this thesis, the improvements need to be finished are analyzed, and the direction of future work is given.