Biometric Detection in Fingerprint, Iris, and 2D face using hybrid of IQA and SIFT

Security is the major concern for today’s scenario. A high level industry uses passwords like thumb, face, voice, iris, etc. So many security systems are available, but not reliable. The biometric details are very useful and essential one of the developing security world, but the biometric details are made fake by the hackers. There are so many methods used to authentication the biometric details in both the hardware and software base. In one of the previous method is Image quality assessment (IQA). In this method extract the eleven qualities of the biometric images. These image qualities features are used to classify the authentication process. In this paper we combine the both IQA and Scale-invariant feature transform (SIFT) and use the Quadratic Discriminate Analysis (QDA).In this paper first we extract the eleven image quality features and extract the SIFT matching points of the image then combine the both features. QDA classifier is used to classify the authentication process of biometric details by using both the extract features of IQA and SIFT. This method is well to detect the authentication of biometric details, if it was spoofed by hackers.