Iris recognition algorithm based on kernel principal component analysis
Yanhua Zhang · Journal of Lanzhou University of Technology · 2007
Having unique priority in biometric testing,iris recognition possesses an important widely application value in security control of places or resources.Iris recognition is composed of some processes such as iris localization,extraction of texture feature,and mode matching.A new iris recognition algorithm was proposed,where kernel principal component analysis(KPCA) was used to extract iris texture feature and a competitive learning mechanism was employed to choose the best feature from KPCA,then forming the codes of iris.Finally,the iris would be recognized by calculating Euclidean distance of covariance weighted reciprocal among the codes.The experimental results of iris recognition showed that this new approach exhibited high operational speed,better effect of feature extraction,and strong adaptability to environment.Therefore,it could be used in actual personal identification system.