The Research On Iris Preprocess And Recognition Algorithm

With the rapid development of new computer and network technologies, information security is becoming more and more important than ever. There are two traditional methods for identification. The first is using certain information such an passwords and PIN numbers. The second is using certain thing such as a key. The two methods are unreliable, because the key may be lost and the password may be forgotten. Biometrics is a kind of science of using individual personal characteristics to verify identity. There are some mature and widespread biometric identification technologies, such as: fingerprint identification, Facial Detection, iris identification, retina identification, palm identification, voice identification, signature identification.All of the biometric identification technologies have almost the same recognition process. The first work is sample collection. The sample could be images of faces, or records of voices, or fingerprints. Then extract the unique feature from the samples, and calculate codes from a specific algorithm. The codes will be stored in a database.If there is a new sample need to be identified, we lookup the database to match the code.Irises possess distinct features for uniquely identifying a person. Iris-Identification is regarded as a kind of noninvasive human identification technique. This paper elucidates in detail the principles and the typical structure of Iris-Identification system. The system includes four parts: segmentation, normalization, feature extraction and recognition.Image preprocessing begins with precisely locating its inner and outer boundaries. We find the threshold form the histogram which can separate the pupil from the image. After that we perform edge detection algorithm based on the Canny operator, then select the points in the edge to perform Point Hough Transform to make recognize the outer boundary. Then center deviation of iris boundaries is normalized. At last we make histogram equalization to approach contrast enhancement.By using discrete 2D Gabor filters, we get relative coefficients from the filtered images. Then the pattern matching algorithm based on weight Hamming distance is discussed for iris code.Experiment results for our iris database prove that the proposed approach for iris recognition is effective. We have got the accuracy rate more than 95%.