Condition of eye images with a low lighting or low contrast ratio between the iris and pupil is one of the challenges for iris recognition in a non-cooperative environment and under visible wavelength illumination. Incorrect iris localization can affect the performance of the iris recognition system. Iso-contrast limited adaptive histogram equalization is proposed to overcome this challenge and increase the performance of iris localization. The eye image is partitioned into the contextual sub-region; then, the proposed method transfers the pixel intensity by referring to a local intensity histogram and a newly suggested cumulative distribution function. This research was tested on 1000 eye images from the UBIRIS.v2 dataset. The results showed that the proposed method performed better than existing methods when dealing with a low lighting or low contrast ratio between the iris and pupil in the eye image.

A low lighting or contrast ratio visible iris recognition using iso-contrast limited adaptive histogram equalization

In this paper, a low complexity technique is proposed for iris recognition in the curvelet transform domain. The proposed method does not require the detection of outer boundary and decreases unwanted artefacts such as the eyelid and eyelash. Thus, the time required for preprocessing of an iris image is significantly reduced. The zero-crossings of the transform coefficients are used to generate the iris codes. Since only the coefficients from approximation subbands are used, it reduces the length of the code. The iris codes are matched employing the correlation coefficient. Extensive experiments are carried out using a number of standard databases such as CASIA- V3, UBIRIS.v1 and UPOL. The results reveal that the proposed method using the curvelet transform provides a very high degree of accuracy (about 100%) over a wide range of images with a low equal error rate (EER) and a significant reduction in the computational time, as compared to those of the state-of-the-art techniques.

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Low complexity iris recognition using curvelet transform

The conventional iris recognition methods do not perform well for the datasets where the eye image may contain nonideal data such as specular reflection, off-angle view, eyelid, eyelashes and other artifacts. This paper gives contributions for a reliable iris recognition method using a new scale-, shift- and rotation-invariant feature-extraction method in time-frequency and spatial domains. Indeed, a 2-level nonsubsampled contourlet transform (NSCT) is applied on the normalized iris images and a gray level co-occurrence matrix (GLCM) with 3 different orientations is computed on both spatial image and NSCT frequency subbands. Moreover, the effect of the occluded parts is reduced by performing an iris localization algorithm followed by a four regions of interest (ROI) selection. The extracted feature set is transformed and normalized to reduce the effect of extreme values in the feature vector. Next, significant features for iris recognition are selected by a two-step method composed by a filtering stage and wrapper based selection. Finally, the selected feature set is classified using support vector machine (SVM). The proposed iris identification method was tested on the public iris datasets CASIA Ver.1 and CASIA Ver.4-lamp showing a state-of-the-art performance.

Iris Recognition using Robust Localization and Nonsubsampled Contourlet Based Features

Image denoising is one of the fundamental image processing problem. Images are corrupted with additive white Gaussian noise during image acquisition and transmission over analog circuits. In medical images the prevalence of noise can be perceived as tumours or artefacts and can lead to first diagnosis. Similarly in satellite images the visibility of images is significantly degraded due to noise, hence the image denoising is of vital importance. There are many denoising mechanisms given in literature are able to work well on lower noise levels but their performance degrades with increasing noise levels. If higher amount of filtering is applied it leads to degradation or removal of edges from the image and hence significant information. In this paper, we proposed an algorithm in which we are able to address the problem of image denoising at higher noise levels while preserving the edge information. The standard bilateral filter does not provides good results at higher noise levels. Hence we proposed to combine robust bilateral filtering with anisotropic diffusion filtering as the anisotropic diffusion perform the smoothing of homogenous regions without blurring the edges. Experimental results show that the proposed method works better for higher Nosie levels in terms of PSNR values and Visual quality.

An Efficient Image Denoising Scheme for Higher Noise Levels using Spatial Domain Filters

Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system.

https://www.mdpi.com/1424-8220/20/5/1308/pdf

Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition.

In iris biometric system, iris images with poor quality possibly lead to poor performance in personal identification or recognition. To address the issue we propose the fast evaluation method for iris quality by sharpness in this paper. In our method, ROI (Region of Interests) is located firstly and then Brenner operator is utilized to evaluate quality of iris images. Experimental results reveal that our proposal has less computation load and high processing speed than compared methods. So it is necessary to evaluate the iris image quality before recognition, and sharpness evaluation is one important part of it. As some previous algorithms used the method of locating first then extracting the region of interest takes more time, this paper proposes a faster approach to evaluate the definition of iris image. First extract the region of interest (ROI) based on experience, then use brenner operator to evaluate the iris image's definition. Experimental results reveal that our proposal has the characteristics of less calculation, high velocity and more effectiveness.

The fast iris image clarity evaluation based on Brenner

We proposed a new normalization method for iris recognition, which is different from the conventional one in which the annular iris region is unwrapped to a rectangular block under polar coordinate. In this method, we investigate the effect of interpolation and decimation in conventional normalization method to recognition rate for the first time. We used the original texture to fill the pupil area, then a novel normalized image can be obtained with the geometric structure and directional information of original iris image well preserved, which enables us to choose simpler features than before. Subsequently, we extracted the multi-direction and multi-scale information feature of normalized iris image by contourlet transform, and adopt SVM to classify the features. Experimental results validate the improvement of recognition rate. required. Thus the geometric structure and the directional information of iris image are altered, and the recognition performance may be affected if texture and directional information are adopted as features. However, the effect has not been studied previously. The purpose of the letter is to study the distortion of the polar coordinate transform to the recognition rate. In order to validate and attempt to alleviate this effect, we proposed another different normalization method without adopting the polar coordinate transformation, thus the original geometric structure and directional information can be preserved. We also propose to use a simple feature that is derived from multi-scale and multi-direction local information so that we can highlight the effect of the normalization step to the recognition rate. After this preprocessing procedure, we use contourlet transform to extract the multi-direction and multi-scale information feature of normalized iris image, and then use support-vector-machine (SVM) to classify the features and give the classification result (8). Contourlet transform is a typical multi-scale and multi-direction system (7). It contains Laplacian Pyramid to achieve multi-scale property and Direction Filter Bank to achieve multi-direction characteristic. SVMs belong to a family of generalized linear classifiers. A special property is that they simultaneously minimize the empirical classification error and maximize the geometric margin. A comparison of our proposed method with existing traditional method is presented, which confirms that interpolation and decimation operations will indeed affect recognition accuracy when texture and direction information are used as features. Experimental results show that the recognition rate can be improved by approximately 30% compared with conventional methods with simple feature being used.

Iris Recognition Based on a Novel Normalization Method and Contourlet Transform

Non-Local Means (NLM) is currently a leading approach in the image de-noising field. It uses the weighted average of all pixels in an image to recover the noise-free pixel values. However, if the pixel values used for average do not lie in the same gray levels as the one to be restored, they will make negative contributions to the de-noising performance. In this paper, we present an adaptive search based image de-noising method that combines the Non-Local Means algorithm with a locally adaptive anisotropic estimation technique, which can select the "right" pixels for NLM averaging process. Experiment results show that the proposed algorithm has superior performance than the original one in terms of PSNR、SSIM, and also in visual quality.

Adaptive Search Based Non-Local Means Image De-Noising

During recent years, iris recognition has been accepted by public as a high-discriminative personal identification technology duo to its unique, stable and noninvasiveness properties. However, eyelash occlusion degrades iris equality and leads low recognition rate of nonideal iris. In this paper we propose a novel eyelash detection method based on expectation maximization (EM) algorithm and Gaussian Mixture Model (GMM). This method is robust, and does not rely on the fixed threshold and the detection of eyelid. Experimental results depict this method can detect eyelashes accurately and effectively.

A Robust and Fast Eyelash Detection Basted on Expectation Maximization and Gaussian Mixture Model

In iris recognition system, the clarity of iris image is an important factor that influences recognition effect. In the process of recognition, the blurred image may possibly be rejected by the automatic iris recognition system, which will lead to the failure of identification. Therefore it is necessary to evaluate the iris image definition before recognition. Considered the existing evaluation methods on iris image definition, we proposed a fast algorithm to evaluate the definition of iris image in this paper. In our algorithm, firstly ROI (Region of Interest) is extracted based on the reference point which is determined by using the feature of the light spots within the pupil, then Tenengrad operator is used to evaluate the iris image’s definition. Experiment results show that, the iris image definition algorithm proposed in this paper could accurately distinguish the iris images of different clarity, and the algorithm has the merit of low computational complexity and more effectiveness.

Min Han

Papers

The fast iris image clarity evaluation based on Brenner

Iris Recognition Based on a Novel Normalization Method and Contourlet Transform

Adaptive Search Based Non-Local Means Image De-Noising

A Robust and Fast Eyelash Detection Basted on Expectation Maximization and Gaussian Mixture Model

The fast iris image clarity evaluation based on Tenengrad and ROI selection