Iris Image Quality Research Articles

Iris Matching Based on Personalized Weight Map

Iris recognition typically involves three steps, namely, iris image preprocessing, feature extraction, and feature matching. The first two steps of iris recognition have been well studied, but the last step is less addressed. Each human iris has its unique visual pattern and local image features also vary from region to region, which leads to significant differences in robustness and distinctiveness among the feature codes derived from different iris regions. However, most state-of-the-art iris recognition methods use a uniform matching strategy, where features extracted from different regions of the same person or the same region for different individuals are considered to be equally important. This paper proposes a personalized iris matching strategy using a class-specific weight map learned from the training images of the same iris class. The weight map can be updated online during the iris recognition procedure when the successfully recognized iris images are regarded as the new training data. The weight map reflects the robustness of an encoding algorithm on different iris regions by assigning an appropriate weight to each feature code for iris matching. Such a weight map trained by sufficient iris templates is convergent and robust against various noise. Extensive and comprehensive experiments demonstrate that the proposed personalized iris matching strategy achieves much better iris recognition performance than uniform strategies, especially for poor quality iris images.

A novel approach to image quality assessment in iris recognition systems

With increasing needs in security systems, iris recognition is an important technique as one of the most reliable solutions for biometrics-based identification systems. However, not all of the iris images acquired from the device are in-focus and sharp enough for recognition. Thus, the poor quality of iris images has serious influence on the accuracy of iris recognition. Sometimes these images are not good enough due to a variety of factors: defocus blur, motion blur, eyelid occlusion and eyelash occlusion. This paper presents an approach for quality assessment of iris images, which can select the high quality iris images from the image sequences to be used in iris recognition systems. First, the gradient information of the iris regions (64 × 64) adjoining the pupil on the right and left sides is calculated to distinguish the blurred images from the in-focus images. Next, the valid iris regions are employed to discriminate between the occluded images and useful images. We present underlying theory as well as experimental results from both the CASIA iris database and the database provided for the iris challenge evaluation (ICE). The results show that this evaluation approach can actually reflect the real quality of iris images and significantly improve the overall performance of the iris recognition systems.

Estimating and Fusing Quality Factors for Iris Biometric Images

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is one of the most reliable biometrics in terms of recognition and identification performance. However, the performance of these systems is affected by poor-quality imaging. In this paper, we extend iris quality assessment research by analyzing the effect of various quality factors such as defocus blur, off-angle, occlusion/specular reflection, lighting, and iris resolution on the performance of a traditional iris recognition system. We further design a fully automated iris image quality evaluation block that estimates defocus blur, motion blur, off-angle, occlusion, lighting, specular reflection, and pixel counts. First, each factor is estimated individually, and then, the second step fuses the estimated factors by using a Dempster-Shafer theory approach to evidential reasoning. The designed block is evaluated on three data sets: Institute of Automation, Chinese Academy of Sciences (CASIA) 3.0 interval subset, West Virginia University (WVU) non-ideal iris, and Iris Challenge Evaluation (ICE) 1.0 dataset made available by National Institute for Standards and Technology (NIST). Considerable improvement in recognition performance is demonstrated when removing poor-quality images selected by our quality metric. The upper bound on computational complexity required to evaluate the quality of a single image is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> log n).

Feature correlation evaluation approach for iris feature quality measure

It is challenging to develop an iris image quality measure to determine compressed iris image quality. The compression process introduces new artificial patterns while suppressing existing iris patterns. This paper proposes a feature correlation evaluation approach for iris image quality measure, which can discriminate the artificial patterns from the natural iris patterns and can also measure iris image quality for uncompressed images. The experimental results show that the proposed method could objectively perform quality measure on both non-compressed and compressed images.

A new iris segmentation method for non-ideal iris images

Many researchers have studied iris recognition techniques in unconstrained environments, where the probability of acquiring non-ideal iris images is very high due to off-angles, noise, blurring and occlusion by eyelashes, eyelids, glasses, and hair. Although there have been many iris segmentation methods, most focus primarily on the accurate detection with iris images which are captured in a closely controlled environment. This paper proposes a new iris segmentation method that can be used to accurately extract iris regions from non-ideal quality iris images. This research has following three novelties compared to previous works; firstly, the proposed method uses AdaBoost eye detection in order to compensate for the iris detection error caused by the two circular edge detection operations; secondly, it uses a color segmentation technique for detecting obstructions by the ghosting effects of visible light; and thirdly, if there is no extracted corneal specular reflection in the detected pupil and iris regions, the captured iris image is determined as a “closed eye” image. The proposed method has been tested using the UBIRIS.v2 database via NICE.I (Noisy Iris Challenge Evaluation – Part I) contest. The results show that FP (False Positive) error rate and FN (False Negative) error rate are 1.2% and 27.6%, respectively, from NICE.I report (the 5th highest rank).

A ROBUST IRIS LOCALIZATION ALGORITHM VIA RADIAL SYMMETRY FOR NONIDEAL CAPTURING CONDITION

Iris localization is a key component of practical iris recognition system. Previous algorithms show good localization performances for iris images captured in the ideal conditions. However, in practice, the quality of iris image is greatly influenced by luminance, eyelashes, hair or glasses frame, which will cause mislocalization. In order to improve the robustness of iris localization, this paper proposes a new localization algorithm based on the radial symmetry transform, in which the radial symmetry characteristic of the pupil is utilized to realize iris localization. Experimental results show that the proposed algorithm can efficiently avoid the interference of luminance and other bad conditions, and realize robust precise localization in a real-time system.

Iris quality assessment and bi-orthogonal wavelet based encoding for recognition

Iris recognition has been demonstrated to be an efficient technology for personal identification. In this work, methods to perform iris encoding using bi-orthogonal wavelets and directional bi-orthogonal filters are proposed and compared. All the iris images are enhanced using the wavelet domain in-band de-noising method. This method is shown to improve the iris segmentation results. A framework to assess the iris image quality based on occlusion, contrast, focus and angular deformation is introduced and used as part of a novel adaptive matching technique based on the assessed iris image quality. Adaptive matching presents improved performance when compared against the Hamming distance method. Four different databases are used to analyze the system performance. The first two databases include popular CASIA and high resolution University of Bath databases. Results obtained for these databases compare with results from the literature, in terms of speed as well as accuracy. The other two databases have challenging off-angle (WVU database) and uncontrolled (Clarkson database) iris images and are used to assess the limits of system performance. Best results are achieved for directional bi-orthogonal filter based encoding technique combined with the adaptive matching method with EER values of 0.07%, 0.15%, 0.81% and 1.29% for the four databases, which reflect highly competent performance and high correlation with the quality of the iris images.

New focus assessment method for iris recognition systems

In this paper, we propose a new focus assessment method (focus method) for iris recognition systems, which combines the wavelet transform method and the SVM (support vector machine) [Vapnik, V., 1998. Statistical Learning Theory. John Wiley & Sons, NY, USA]. The wavelet-based method estimates focus values by using the ratio of high and low-frequency sub-band averages. The SVM find optimal decision boundary between focused and defocused image with image brightness and focus value as input values. The proposed method has shown distinctive advantages in terms of the following four points. First, by using the proposed wavelet-based method, it detects omni-directional high-frequency which is the characteristic of iris patterns. Second, proposed method can detect slight defocusing of images by using the average of high and low-frequency sub-bands simultaneously. Third, the SVM reduces the error rate of the wavelet-based method by finding the optimum threshold. Fourth, processing time is reduced. It is 213 times faster than the maximum speeds compared with previous spatial domain methods [Kang, B., Park, K., 2006. A study on fast iris restoration based on focus checking. In: LNCS (AMDO 2006), vol. 4069, pp. 19–28; Daugman, J.G., 2004. How iris recognition works. IEEE Trans. Circuits Systems Video Technol. 14, 21–30; Wei, Z., Tan, T., Sun, Z., Cui, J., 2005. Robust and Face Assessment of Iris Image Quality. ICB. pp. 464–471]. For making the reference criteria to compare with the result of focus measures, we propose a schematic model for evaluating the pixel size of SR (specular reflection) in the cornea. To compare with the performance rate, Daugman’s kernel, Kang’s kernel, Wei’s kernel and Kautsky’s method are tested with the proposed method using five different types of data. The proposed method showed the best performance and has shown 3.03% of ER (error rate) using the Yonsei database and 2.62% of ER using the UBIRIS.

Automated personal identification system based on human iris analysis

In general, a typical iris recognition system includes iris imaging, iris liveness detection, iris image quality assessment, and iris recognition. This paper presents an algorithm focusing on the last two steps. The novelty of this algorithm includes improving the speed and accuracy of the iris segmentation process, assessing the iris image quality such that only the clear images are accepted so as to reduce the recognition error, and producing a feature vector with discriminating texture features and a proper dimensionality so as to improve the recognition accuracy and computational efficiency. The Hough transform, polynomial fitting technique, and some morphological operations are used for the segmentation process. The phase data from 1D Log-Gabor filter is extracted and encoded efficiently to produce a proper feature vector. Experimental tests were performed using CASIA iris database (756 samples). These tests prove that the proposed algorithm has an encouraging performance.

Improving Iris Recognition Accuracy Via Cascaded Classifiers

As a reliable approach to human identification, iris recognition has received increasing attention in recent years. The most distinguishing feature of an iris image comes from the fine spatial changes of the image structure. So iris pattern representation must characterize the local intensity variations in iris signals. However, the measurements from minutiae are easily affected by noise, such as occlusions by eyelids and eyelashes, iris localization error, nonlinear iris deformations, etc. This greatly limits the accuracy of iris recognition systems. In this paper, an elastic iris blob matching algorithm is proposed to overcome the limitations of local feature based classifiers (LFC). In addition, in order to recognize various iris images efficiently a novel cascading scheme is proposed to combine the LFC and an iris blob matcher. When the LFC is uncertain of its decision, poor quality iris images are usually involved in intra-class comparison. Then the iris blob matcher is resorted to determine the input iris' identity because it is capable of recognizing noisy images. Extensive experimental results demonstrate that the cascaded classifiers significantly improve the system's accuracy with negligible extra computational cost.

Iris Recognition Using Wavelet Features

The traditional iris recognition systems require equal high quality human iris images. A cheap image acquisition system has difficulty in capturing equal high quality iris images. This paper describes a new feature representation method for iris recognition robust to noises. The disc-shaped iris image is first convolved with a low pass filter along the radial direction. Then, the radially smoothed iris image is decomposed in the angular direction using a one-dimensional continuous wavelet transform. Each decomposed one-dimensional waveform is approximated by an optimal piecewise linear curve connecting a small set of node points. The set of node points is used as a feature vector. The optimal approximation procedure reduces the feature vector size while maintaining recognition accuracy. The similarity between two iris images is measured by the normalized cross-correlation coefficients between optimal curves. The similarity between two iris images is estimated using mid-frequency bands. The rotation of one-dimensional signals due to the head tilt is estimated using the lowest frequency component. Experimentally we show the proposed method produces superb performance in iris recognition.