A New Partitioning Method in Frequency Analysis of the Retinal Images for Human Identification

Abstract

Retinal image is one of the robust and accurate biometrics methods to recognize a person. In this article we present a new biometric identification system based on Fourier transform and angular partitioning of the spectrum. In this method, at first, the optical disc is localized using template matching technique and used for rotating the retinal image into the reference position. It compensates the rotation effects which might occur during the scanning process. Fourier transform coefficient and angular partitioning of these coefficients are used for the purpose of feature definition in our method. The extract features are rotation invariant and robust against noise. Finally we employ Euclidean distance for feature matching. The proposed algorithm was tested using 40 images from DRIVE database and experimental results showed the efficiency of the proposed algorithm for the identification of retinal images with noise and rotation

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

“…Köse et al [22] proposed a retinal identification that employed a similarity measure and is capable of tolerating the transformations. In [23], Fourier transform coefficient and angular partitioning are used for feature detection. Euclidean distance is used in the matching process.…”

Steganography for retinal biometrics

“…There are different authentication schemes described in [8,12,14,15,16,17]. These authentication schemes are very different to each other.…”

“…The vascular graph is constructed using different approaches which helps to authenticate. In [14] a method has been used based on fourier transforms to evaluate retinal biometrics. The cosine values are taken into consideration.…”

Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform

“…In addition, the angular partition based method [27–29] has also been used in retinal identification. In [27] the authors claimed that preprocessing based on blood vessel extraction increases the computational time, and proposed a new feature extraction method without any preprocessing phase based on angular partitioning of the spectrum.…”

“…In [27] the authors claimed that preprocessing based on blood vessel extraction increases the computational time, and proposed a new feature extraction method without any preprocessing phase based on angular partitioning of the spectrum. Another approach that uses angular partitioning for identifying retinal images claimed that the identification task in their approach is invariant from the most of the common affine transformations and is suitable for real time applications [28].…”