Iris Feature Extraction Algorithm Research Articles

An algorithm of Iris feature-extracting based on 2D Log-Gabor

This paper proposes a new algorithm for iris feature extraction. The algorithm uses the odd symmetric 2D Log-Gabor filter to analyze the phase and amplitude of iris texture with respect to different frequencies and orientations, and uses feature fusion to eliminate redundant feature. It successfully solves such problems as the crescent-shaped spectrum and grid-like patterns which will appear when 2D Log-Gabor wavelet is used to extract iris features. The experimental results also show that the algorithm proposed has strong robustness against noise and interference, and has important theoretical value and practical significance.

Iris Feature Extraction based on Haar Wavelet Transform

To improve the accuracy of iris recognition system, we propose an efficient algorithm for iris feature extraction based on 2D Haar wavelet. Firstly, the iris image is decomposed by the 2D Haar wavelet three times, and then a 375-bit iris code is obtained by quantizing all the high-frequency coefficients at third lever. Finally we use similarity degree function as matching scheme. Experimental results on CASIA iris database show that our algorithm has the encouraging correct recognition rate (CRR) which is only 99.18%, accompanying with very low equal error rate (EER) 0.54%.

Iris Feature Extraction Algorithm Valuation Based on the Fuzzy Clustering Evaluation Model

Iris Feature Extraction Algorithm Evaluation is an important link of evaluation on iris identification system. For the need of optimal feature extraction algorithm according to the identification, this thesis provides a evaluation scheme based on the fuzzy clustering model, and builds a relevant evaluation model, which proves to make a scientific evaluation on the algorithm in the phase of feature extraction.

Iris feature extraction algorithm based on odd symmetry 2D Log-Gabor

When 2D Log-Gabor extracted iris features,part of them are non-effective.Aiming at solving this problem,a new algorithm using odd symmetry 2D Log-Gabor filter to extract iris feature was proposed.Based on the study of odd symmetry 2D Log-Gabor filter,this paper analyzed the phase position and amplitude of iris texture in different frequency and directions,and eliminated redundant feature by feature integration.This algorithm solves the defects of 2D Log-Gabor wavelet extract iris feature problem.The experimental results show that the odd symmetry 2D Log-Gabor filters have strong robustness with noise and interferences.This algorithm has high theoretical and practical value.

An image segmentation based method for iris feature extraction

In this article, the local anomalistic blocks such as crypts, furrows, and so on in the iris are initially used directly as iris features. A novel image segmentation method based on intersecting cortical model (ICM) neural network was introduced to segment these anomalistic blocks. First, the normalized iris image was put into ICM neural network after enhancement. Second, the iris features were segmented out perfectly and were output in binary image type by the ICM neural network. Finally, the fourth output pulse image produced by ICM neural network was chosen as the iris code for the convenience of real time processing. To estimate the performance of the presented method, an iris recognition platform was produced and the Hamming Distance between two iris codes was computed to measure the dissimilarity between them. The experimental results in CASIA v1.0 and Bath iris image databases show that the proposed iris feature extraction algorithm has promising potential in iris recognition.

Local feature extraction for iris recognition with automatic scale selection

This paper presents an iris recognition system using automatic scale selection algorithm for iris feature extraction. The proposed system first filters the given iris image adopting a bank of Laplacian of Gaussian (LoG) filters with many different scales and computes the normalized response of every filter. The parameter γ used to normalize the filter responses, is derived by analyzing the scale-space maxima of the blob feature detector responses. Then the maxima normalized response over scales for each point are selected together as the optimal filter outputs of the given iris image and the binary codes for iris feature representation are achieved by encoding these optimal outputs through a zero threshold. Comparison experiment results clearly demonstrate an efficient performance of the proposed algorithm.