Byung Jun Kang

A robust eyelash detection based on iris focus assessment

For accurate iris recognition, it is essential to detect eyelash regions and remove them for iris code generation, since eyelashes act as noise factors in the iris recognition. In addition, eyelash positions can be changed for enrollment and recognition and this may cause FR (false rejection). To overcome these problems, we propose a new method for detecting eyelashes in this paper. This work shows three advantages over previous works. First, because eyelash detection was performed based on focus assessment, its performance was not affected by image blurring. Second, the new focus assessment method is appropriate for iris images. Third, the detected eyelash regions were not used for iris code generation and therefore iris recognition accuracy was greatly enhanced. Experimental results showed that the eyelash detection error was about 0.96% when using the CASIA DB and iris recognition accuracy with eyelash detection was enhanced more than 0.86% of EER when compared to the EER obtained without eyelash detection.

Real-Time Image Restoration for Iris Recognition Systems

In the field of biometrics, it has been reported that iris recognition techniques have shown high levels of accuracy because unique patterns of the human iris, which has very many degrees of freedom, are used. However, because conventional iris cameras have small depth-of-field (DOF) areas, input iris images can easily be blurred, which can lead to lower recognition performance, since iris patterns are transformed by the blurring caused by optical defocusing. To overcome these problems, an autofocusing camera can be used. However, this inevitably increases the cost, size, and complexity of the system. Therefore, we propose a new real-time iris image-restoration method, which can increase the cameras DOF without requiring any additional hardware. This paper presents five novelties as compared to previous works: (1) by excluding eyelash and eyelid regions, it is possible to obtain more accurate focus scores from input iris images; (2) the parameter of the point spread function (PSF) can be estimated in terms of camera optics and measured focus scores; therefore, parameter estimation is more accurate than it has been in previous research; (3) because the PSF parameter can be obtained by using a predetermined equation, iris image restoration can be done in real-time; (4) by using a constrained least square (CLS) restoration filter that considers noise, performance can be greatly enhanced; and (5) restoration accuracy can also be enhanced by estimating the weight value of the noise-regularization term of the CLS filter according to the amount of image blurring. Experimental results showed that iris recognition errors when using the proposed restoration method were greatly reduced as compared to those results achieved without restoration or those achieved using previous iris-restoration methods.

A study on iris image restoration

Because iris recognition uses the unique patterns of the human iris, it is essential to acquire the iris images at high quality for accurate recognition. Defocusing reduces the quality of the iris image and the performance of iris recognition, consequently. In order to acquire a focused iris image at high quality, an iris recognition camera must control the focal length of the moving lens. However, that causes the cost and size of iris camera to be increased and that needs complicated auto-focusing algorithm, also. To overcome such problems, we propose new method of iris image restoration. Experimental results show that the total recognition time is reduced as much as 390ms on average with the proposed restoration algorithm.

A study on iris localization and recognition on mobile phones

A new iris recognition method for mobile phones based on corneal specular reflections (SRs) is discussed. We present the following three novelties over previous research. First, in case of user with glasses, many noncorneal SRs may happen on the surface of glasses and it is very difficult to detect genuine SR on the cornea. To overcome such problems, we propose a successive on/off dual illuminator scheme to detect genuine SRs on the corneas of users with glasses. Second, to detect SRs robustly, we estimated the size, shape, and brightness of the SRs based on eye, camera, and illuminator models. Third, the detected eye (iris) region was verified again using the AdaBoost eye detector. Experimental results with 400 face images captured from 100 persons with a mobile phone camera showed that the rate of correct iris detection was 99.5 (for images without glasses) and 98.9 (for images with glasses or contact lenses). The consequent accuracy of iris authentication was 0.05 of the EER (equal error rate) based on detected iris images.

A study on eyelid localization considering image focus for iris recognition

This paper proposes a robust detection algorithm that can be used to detect eyelid region for iris recognition. This research has the following four advantages and contributions compared to the previous works. First, we interpolate the detected eyelashes and specular reflections, which enable us to determine a more accurate searching area of eyelid. Second, we are able to define a limited eyelid searching area by finding the cross position between the eyelids and the outer boundary of the irises. Third, by using eyelid detection mask considering focus value, we can enhance the eyelid detection performance even in the case of defocused iris image. Fourth, by applying the rotation term into the parabolic Hough transform which fits the detected eyelid candidate points, we can detect the accurate eyelid position even in the case of rotated eye. As the experimental results show, the detection accuracy rates were 91.33% and 98.45% when detecting the upper and lower eyelids, respectively.

Multimodal biometric method that combines veins, prints, and shape of a finger

Multimodal biometrics provides high recognition accuracy and population coverage by using various biometric features. A single finger contains finger veins, fingerprints, and finger geometry features; by using multimodal biometrics, information on these multiple features can be simultaneously obtained in a short time and their fusion can outperform the use of a single feature. This paper proposes a new finger recognition method based on the score-level fusion of finger veins, fingerprints, and finger geometry features. This research is novel in the following four ways. First, the performances of the finger-vein and fingerprint recognition are improved by using a method based on a local derivative pattern. Second, the accuracy of the finger geometry recognition is greatly increased by combining a Fourier descriptor with principal component analysis. Third, a fuzzy score normalization method is introduced; its performance is better than the conventional Z-score normalization method. Fourth, finger-vein, fingerprint, and finger geometry recognitions are combined by using three support vector machines and a weighted SUM rule. Experimental results showed that the equal error rate of the proposed method was 0.254%, which was lower than those of the other methods.

A new multi-unit iris authentication based on quality assessment and score level fusion for mobile phones

Although iris recognition technology has been reported to be more stable and reliable than other biometric systems, performance can be degraded due to many factors such as small eyes, camera defocusing, eyelash occlusions and specular reflections on the surface of glasses. In this paper, we propose a new multi-unit iris authentication method that uses score level fusion based on a support vector machine (SVM) and a quality assessment method for mobile phones. Compared to previous research, this paper presents the following two contributions. First, we reduced the false rejection rate and improved iris recognition accuracy by using iris quality assessment. Second, if even two iris images were determined to be of bad quality, we captured the iris images again without using a recognition process. If only one iris image among the left and right irises was regarded as a good one, it was used for recognition. However, if both the left and right iris images were good, we performed multi-unit iris recognition using score level fusion based on a SVM. Experimental results showed that the accuracy of the proposed method was superior to previous methods that used only one good iris image or those methods that used conventional fusion methods.

Finger vein recognition using weighted local binary pattern code based on a support vector machine

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.

Multimodal biometric authentication based on the fusion of finger vein and finger geometry

We propose a new multimodal biometric recognition based on the fusion of finger vein and finger geometry. This research shows three novelties compared to previous works. First, this is the first approach to combine the finger vein and finger geometry information at the same time. Second, the proposed method includes a new finger geometry recognition based on the sequential deviation values of finger thickness extracted from a single finger. Third, we integrate finger vein and finger geometry by a score-level fusion method based on a support vector machine. Results show that recognition accuracy is significantly enhanced using the proposed method.

Restoration of motion-blurred iris images on mobile iris recognition devices

With the widespread use of biometric technology in many applications, iris recognition techniques on mobile devices have been more commercialized recently. Some of these techniques include the Pier 2.3 system and the HIIDE Series 4. When working with mobile devices, the shaking movements of a given users hand can sometimes cause motion-blurred iris images, which can reduce iris recognition accuracy. We propose a new method of restoring these kinds of motion-blurred iris images. We present three improvements over previous works: (1) We estimated the direction and magnitude of motion, based on the corneal specular reflection of the pupil. (2) The point spread function (PSF) was adaptively modeled based on the estimated motion information. (3) The restoration of motion-blurred iris images was performed with the filter parameters that were determined in terms of increasing the accuracy of iris recognition. Experimental results showed that the equal error rate (EER) without the proposed motion-deblurring method was 1.538% and that with the proposed method was 0.962%. Consequently, the EER was reduced as much as 0.576% (=1.538-0.962%), and results showed that the accuracy of iris recognition with the proposed method was superior to results with conventional motion-deblurring methods.