In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%.

With recent increases in security requirements, biometrics such as fingerprints, faces, and irises have been widely used in many recognition applications including door access control, personal authentication for computers, Internet banking, automatic teller machines, and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins to identify individuals at a high level of accuracy. This article proposes a new finger vein recognition method using minutia-based alignment and local binary pattern (LBP)-based feature extraction. Our study makes three novelties compared to previous works. First, we use minutia points such as bifurcation and ending points of the finger vein region for image alignment. Second, instead of using the whole finger vein region, we use several extracted minutia points and a simple affine transform for alignment, which can be performed at fast computational speed. Third, after aligning the finger vein image based on minutia points, we extract a unique finger vein code using a LBP, which reduces false rejection error and thus the equal error rate (EER) significantly. Our resulting EER was 0.081% with a total processing time of 118.6 ms.

No abstract

No abstract

No abstract

No abstract

Abstract. Recently, it has become necessary to evaluate the performance of display devices in terms of human factors. To meet this requirement, several studies have been conducted to measure the eyestrain of users watching display devices. However, these studies were limited in that they did not consider precise human visual information. Therefore, a new eyestrain measurement method is proposed that uses a liquid crystal display (LCD) to measure a user's gaze direction and visual field of view. Our study is different in the following four ways. First, a user's gaze position is estimated using an eyeglass-type eye-image capturing device. Second, we propose a new eye foveation model based on a wavelet transform, considering the gaze position and the gaze detection error of a user. Third, three video adjustment factors-variance of hue (VH), edge, and motion information-are extracted from the displayed images in which the eye foveation models are applied. Fourth, the relationship between eyestrain and three video adjustment factors is investigated. Experimental results show that the decrement of the VH value in a display induces a decrease in eyestrain. In addition, increased edge and motion components induce a reduction in eyestrain.

Abstract. Fake iris detection is to detect and defeat a fake (forgery) iris image input. To solve the problems of previous researches on fake iris detection, we propose the new method of detecting fake iris attack based on the Purkinje image. Especially, we calculated the theoretical positions and distances between the Purkinje images based on the human eye model and the performance of fake detection algorithm could be much enhanced by such information. Experimental results showed that the FAR (False Acceptance Rate for accepting fake iris as live one) was 0.33% and FRR(False Rejection Rate of rejecting live iris as fake one) was 0.33%.