Abstract
Feature vector generation is an important step in biometric authentication. Biometric traits such as fingerprint, finger-knuckle prints, palmprint, and iris are rich in texture. This texture is unique and the feature vector extraction algorithm should correctly represent the texture pattern. In this paper a texture feature extraction methodology is proposed for these biometric traits. This method is based on one step transform of the two dimensional images and then using the intermediate transformation data to generate complex planes for feature vector generation. This method is implemented using Walsh, DCT, Hartley, Kekre Transform & Kekre Wavelets. Results indicate the effectiveness of the feature vector for biometric authentication.
Highlights
Biometric Authentication systems take the advantage of the uniqueness of the human body
In this paper we have proposed a feature vector extraction method based on intermediate Walsh transform
The details of Performance Index (PI) & classification ratio (CCR) (Correct Classification Ratio) of above mentioned feature vectors are summarized in Fig. 8, 9 & 10
Summary
Biometric Authentication systems take the advantage of the uniqueness of the human body. They derive the classifying function from what a person is than what a person carries (like smartcard, token etc.). Biometrics comprises methods for uniquely recognizing humans based upon one or more intrinsic physical and/or behavioral traits. In particular, biometrics is used as a form of identity access management and access control. It is used to identify individuals in groups that are under surveillance [1]. Biometric characteristics can be divided in two main classes:
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