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Kang Ryoung Park

Bio: Kang Ryoung Park is an academic researcher from Dongguk University. The author has contributed to research in topics: Iris recognition & Gaze. The author has an hindex of 45, co-authored 332 publications receiving 6878 citations. Previous affiliations of Kang Ryoung Park include Sangmyung University & Yonsei University.


Papers
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Journal ArticleDOI
16 Mar 2017-Sensors
TL;DR: The experimental results show that the proposed person recognition method using the information extracted from body images is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.
Abstract: The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

335 citations

Journal ArticleDOI
TL;DR: A new age estimation method using a hierarchical classifier method based on both global and local facial features is proposed, which was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.

262 citations

Journal IssueDOI
TL;DR: A new finger vein recognition method using minutia-based alignment and local binary pattern (LBP)-based feature extraction, which reduces false rejection error and thus the equal error rate (EER) significantly.
Abstract: 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 0.081p with a total processing time of 118.6 ms. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 179–186, 2009

155 citations

Journal ArticleDOI
06 Jun 2017-Sensors
TL;DR: A finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN) is proposed and showed a better performance compared to the conventional methods.
Abstract: Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

149 citations

Journal ArticleDOI
TL;DR: A new method for eye state classification that combines three innovations: extraction and fusion of features from both eyes, initialization of driver-specific thresholds to account for differences in eye shape and texture, and modeling ofDriver-specific blinking patterns for normal (non-drowsy) driving is proposed.
Abstract: Accurate classification of eye state is a prerequisite for preventing automobile accidents due to driver drowsiness. Previous methods of classification, based on features extracted for a single eye, are vulnerable to eye localization errors and visual obstructions, and most use a fixed threshold for classification, irrespective of variations in the driver's eye shape and texture. To address these deficiencies, we propose a new method for eye state classification that combines three innovations: (1) extraction and fusion of features from both eyes, (2) initialization of driver-specific thresholds to account for differences in eye shape and texture, and (3) modeling of driver-specific blinking patterns for normal (non-drowsy) driving. Experimental results show that the proposed method achieves significant improvements in detection accuracy.

146 citations


Cited by
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Proceedings Article
01 Jan 1999

2,010 citations

Proceedings Article
01 Jan 1989
TL;DR: A scheme is developed for classifying the types of motion perceived by a humanlike robot and equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented.
Abstract: A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

2,000 citations

Book
01 Dec 1988
TL;DR: In this paper, the spectral energy distribution of the reflected light from an object made of a specific real material is obtained and a procedure for accurately reproducing the color associated with the spectrum is discussed.
Abstract: This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

1,401 citations