• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.25-34
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• 2010

Robust face recognition under various illumination environments is difficult to achieve. For robust face recognition with respect to illumination variations, illumination normalization of face images is usually applied as a preprocessing step. Most of previously proposed illumination normalization methods cannot handle cast shadows in face images effectively. In this paper, We propose a new face illumination normalization method based on the illumination-separated face identity texture subspace. Since the face identity texture subspace is constructed so as to be separated from the effects of illumination variations, the projection of face images into the subspace produces a good illumination-normalized face images. Through experiments, it is shown that the proposed face illumination normalization method can effectively eliminate cast shadows as well as attached shadows and achieves a good face illumination normalization.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.179-184
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• 2009

Robust face recognition under various illumination environments is difficult to achieve. For face recognition robust to illumination changes, usually face images are normalized with respect to illumination as a preprocessing step before face recognition. The anisotropic smoothing-based illumination normalization method, known to be one of the best illumination normalization methods, cannot handle casting shadows. In this paper, we present an efficient illumination normalization method for face recognition. The proposed illumination normalization method separates the effect of illumination from eigenfaces and constructs an illumination-separated eigenface subspace. Then, an incoming face image is projected into the subspace and the obtained projected face image is rendered so that illumination effects including casting shadows are reduced as much as possible. Application to real face images shows the proposed illumination normalization method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.49-55
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• 2010

Cast shadow caused by an illumination condition can produce troublesome effects for face recognition system using reflectance image. Consequently, we need to separate cast shadow area from feature area for improvement of recognition accuracy. A Bilateral filter smooths image while preserving edges, by means of a nonlinear combination of nearby pixel values. Processing such characteristics, this method is suited to our purpose in illumination estimation process based on Retinex. Therefore, in this paper, we propose a new illumination normalization method based on the Bilateral filter in face images. The proposed method produces a reflectance image that is preserved relatively exact cast shadow area, because coefficient of filter is designed to multiply proximity and discontinuity of pixels in input image. Performance of our method is measured by a recognition accuracy of principle component analysis(PCA) and evaluated to compare with other conventional illumination normalization methods.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.54-60
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• 2004

Face recognition is one of the problems to be solved by appearance based matching technique. However, the appearance of face image is very sensitive to variation in illumination. One of the easiest ways for better performance is to collect more training samples acquired under variable lightings but it is not practical in real world. ]:n object recognition, it is desirable to focus on feature extraction or normalization technique rather than focus on classifier. This paper presents a simple approach to normalization of faces subject to directional illumination. This is one of the significant issues that cause error in the face recognition process. The proposed method, ICR(illumination Compensation based on Multiple Linear Regression), is to find the plane that best fits the intensity distribution of the face image using the multiple linear regression, then use this plane to normalize the face image. The advantages of our method are simple and practical. The planar approximation of a face image is mathematically defined by the simple linear model. We provide experimental results to demonstrate the performance of the proposed ICR method on public face databases and our database. The experimental results show a significant improvement of the recognition accuracy.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.148-149
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• 2018

카메라 기술의 발달로 나노 단위의 유해물질 영상을 간단한 광학장치를 장착한 휴대폰을 사용해 손쉽게 획득할 수 있게 되었다. 하지만, 유해물질 영상 관찰을 위하여 실제 사용되는 현미경에 비하여는 영상 전역에 원치 않는 잡음이 현저하게 발생한다. 특히 대중적인 저가의 광학계를 사용할 경우, 광량이 불균등하게 조사됨에 따라 얻어진 유해물질 영상에 왜곡이 발생할 수 있는데 이로 인해 기존의 유해물질 농도 검출 알고리즘을 적용하는 경우 좋지 못한 결과를 얻을 수 있다. 따라서 영상 전체에 조사되는 불균형한 조명에 의한 영향을 최소화할 필요가 있으며, 이에 착안하여 본 논문에서는 가우시안 모델에 기반한 조명 정규화 방법을 제안한다. 이는 영상 전역에 발생한 불균형 조명에 대한 영향을 최소화하여 찾고자 하는 유해물질 영역의 경계 특성을 더욱 명확하게 할 수 있는 효과가 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.815-823
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• 2004

A hue-based attention operator and a combinational integral projection function(CIPF) are proposed to extract the normalized regions of face and facial features robustly against illumination variation. The face candidate regions are efficiently detected by using skin color filter, and the eyes are located accurately nil robustly against illumination variation by applying the proposed hue- and symmetry-based attention operator to the face candidate regions. And the faces are confirmed by verifying the eyes with the color-based eye variance filter. The proposed CIPF, which combines the weighted hue and intensity, is applied to detect the accurate vertical locations of the eyebrows and the mouth under illumination variations and the existence of mustache. The global face and its local feature regions are exactly located and normalized based on these accurate geometrical information. Experimental results on the AR face database［8］ show that the proposed eye detection method yields better detection rate by about 39.3% than the conventional gray GST-based method. As a result, the normalized facial features can be extracted robustly and consistently based on the exact eye location under illumination variations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.711-720
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• 2012

In this paper, we introduce the implementation of driver fatigue monitering system and its result. Input video device is selected commercially available web-cam camera. Haar transform is used to face detection and adopted illumination normalization is used for arbitrary illumination conditions. Facial image through illumination normalization is extracted using Haar face features easily. Eye candidate area through illumination normalization can be reduced by anthropometric measurement and eye detection is performed by PCA and Circle Mask mixture model. This methods achieve robust eye detection on arbitrary illumination changing conditions. Drowsiness state is determined by the level on illumination normalize eye images by a simple calculation. Our system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. Our algorithm is implemented with low computation complexity and high recognition rate. We achieve 97% of correct detection rate through in-car environment experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.315-318
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• 2007

자동 얼굴 인식, 표정 인식과 같은 얼굴 영상과 관련된 다양한 연구 분야는 일반적으로 입력 얼굴 영상에 대한 정규화가 필요하다. 사람의 얼굴은 표정, 조명 등에 따라 다양한 형태변화가 있어 입력 영상 마다 정확한 대표 특징 점을 찾는 것은 어려운 문제이다. 특히 감고 있는 눈이나 작은 눈 등은 검출하기 어렵기 때문에 얼굴 관련 연구에서 성능을 저하시키는 주요한 원인이 되고 있다. 이에 다양한 변화에 강건한 눈 검출을 위하여 본 논문에서는 눈의 텍스처 정보를 이용한 눈 검출 방법을 제안한다. 얼굴 영역에서 눈의 텍스처가 갖는 특성을 정의하고 두 가지 형태의 Eye 필터를 정의하였다. 제안된 방법은 Adaboost 기반의 얼굴 영역 검출 단계, 조명 정규화 단계, Eye 필터를 이용한 눈 후보 영역 검출 단계, 눈 위치 점 검출 단계 등 총 4단계로 구성된다. 실험 결과들은 제안된 방법이 얼굴의 자세, 표정, 조명 상태 등에 강건한 검출 결과를 보여주며 감은 눈 영상에서도 강건한 결과를 보여준다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.71-77
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• 2014

In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.423-430
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• 2013

Recently, the personal identification technologies using vein pattern of back of the hand, palm, and finger have been developed actively because it has the advantage that the vein blood vessel in the body is impossible to damage, make a replication and forge. However, it is difficult to extract clearly the vein region from captured vein images through common image prcessing based region segmentation method, because of the light scattering and non-uniform internal tissue by skin layer and inside layer skeleton, etc. Especially, it takes a long time for processing time and makes a discontinuity of blood vessel just in a image because it has non-uniform illumination due to use a locally different adaptive threshold for the binarization of acquired finger-vein image. To solve this problem, we propose illumination normalization based fast method for extracting the finger-vein region. The proposed method has advantages compared to the previous methods as follows. Firstly, for remove a non-uniform illumination of the captured vein image, we obtain a illumination component of the captured vein image by using a low-pass filter. Secondly, by extracting the finger-vein path using one time binarization of a single threshold selection, we were able to reduce the processing time. Through experimental results, we confirmed that the accuracy of extracting the finger-vein region was increased and the processing time was shortened than prior methods.