Modified CNN based Feature Extraction for Sclera Recognition of Off

Abstract

Biometrics are being most unavoidable thing in terms of security and authentication. Sclera vessel patterns are one of the fast-growing biometrics which has unique features as well advantages over the previously discovered biometrics like iris, Finger print, voice, Face, Etc. As the sclera has more advantage over the past since there is a difficulty in segmenting the Sclera region and Extraction of the feature from the segmented sclera portion. Especially in sclera feature extraction the feature of sclera has limited vessel patterns because of the segmentation which eliminates the major portion of the sclera in the process of segmentation. There are several feature extraction methods like Hu moment, Discrete Fourier transforms, Affine Transform, Line descriptor segment, Harris corner, DWT co-efficient has been existing to extract the meaningful feature from the biometrics but here the feature and the behavior of the feature is complex in structure as there is limited number of features, non-linear and Nonstable in thickness. Even the eye is movable which may visibly portion of the vessels in some occasions where the it will be difficult to extract the feature and recognition of the individual.  To overcome this issue a new CNN (Convolutional Neural Network) based feature extraction method has been introduced in this article and the four different version of the CNN has been discussed. The accuracy of the four different CNN based methods were explained in this paper and we scored remarkable response with this modified CNN based feature extraction approach.