The Design of Iris Recognition Classifier Based on Neural Network

Authentication of modern highly information-oriented society , people have penetrated into every aspect of daily life . The same time , due to the rapid development of transportation, communication and network technology , the range of human activities is increasing , the authentication difficulty and importance has become increasingly acute . For such a populous country , authentication has broad application prospects and is of strategic importance . Financial , security , network , e-commerce , and both need reliable authentication . The traditional use of passwords and other authentication method is easily forgotten , counterfeiting and other shortcomings , no longer meet the needs of modern digital society . Advantages of biometric - based biometric authentication technology using human to distinguish the identity of the person , these biometric with a stable , convenient, and difficult to forge , and in recent years has become a hotspot authentication . Commonly used in biometrics include fingerprints , palm prints , iris , face , voice , signature and handwriting . Iris as authentication characteristics , uniqueness , stability , and can acquire resistance , non- invasive advantages . Compared with other methods , the iris having a higher accuracy. According to statistics , the iris recognition error rate is the the various biometric lowest . Therefore , the use of iris authentication technology gradually get the attention of academia and the business community . In this paper, the iris recognition based on neural network classifier design , a detailed description of the iris recognition background , purpose , meaning , the status quo and prospects . Compare the positioning algorithm and feature extraction algorithm , from which a reasonable algorithm to image processing in the the iris digital image processing . In classifier design classifier past are based Hamming Dayton distance classifier design , we propose to design a classifier for iris recognition using neural networks . And successfully scaling rotation, translation and scale invariance iris recognition . Besides, the paper by Image Processing Toolbox and Neural Network Toolbox in MATLAB , introduced in MATLAB Image Processing Toolbox and Neural Network Toolbox in varying degrees .