• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.313-316
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• 2003

This paper proposes a new measurement method of face region size for real-time serveillance systems. The proposed method consists of three steps. In the first step, it detects global face area based on the block-based difference images. In the second step, it measures the face region size using face color information. In the third step, it estimates the face position in the next input image using the trajectory of face regions. The experimental results have shown that the proposed algorithm measures the face size within 20％ relative error on average, which is a]towable for most surveillance systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.19-29
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• 2011

Until now, many face recognition methods have been proposed, most of them use a 1-dimensional feature vector which is vectorized the input image without feature extraction process or input image itself is used as a feature matrix. It is known that the face recognition methods using raw image yield deteriorated performance in databases whose have severe illumination changes. In this paper, we propose a face recognition method using local statistics of gradients and correlations which are good for illumination changes. BDIP (block difference of inverse probabilities) is chosen as a local statistics of gradients and two types of BVLC (block variation of local correlation coefficients) is chosen as local statistics of correlations. When a input image enters the system, it extracts the BDIP, BVLC1 and BVLC2 feature images, fuses them, obtaining feature matrix by $(2D)^2$ PCA transformation, and classifies it with training feature matrix by nearest classifier. From experiment results of four face databases, FERET, Weizmann, Yale B, Yale, we can see that the proposed method is more reliable than other six methods in lighting and facial expression.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4502-4518
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• 2015

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1874-1884
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• 1996

In this paper, we proposed automatic searching algorithm of thresholding value using multilevel thresholding for face segmentation from input bust image effectively. The proposed algorithm extracted the thresholding value of brightness that is formed background region, face region and hair region without illumination, background and face size from input image. The statistical projection analysis project the brightness of multilevel thresholding image into horizontal and vertical direction and decide the thresholding value of face. And the algorithm extracted elliptical type block of face from input image in order to reduce the back ground region and hair region efficiently. The proposed algorithm can reduce searching area of feature extraction and processing time for face recognication.

• Tunnel and Underground Space
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• v.29 no.5
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• pp.332-345
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• 2019

Maximum dip vector of individual joint plane, which can be uniquely defined on the hemispherical projection plane, has been established by considering its dip and dip direction. A new stereographic projection method for the rock slope analysis which employs the maximum dip vector can intuitively predict the failure modes of rock slope. Since the maximum dip vector is uniquely projected on the maximum dip point of the great circle, the sliding direction of discontinuity plane can be recognized directly. By utilizing the maximum dip vector of discontinuity both the plane sliding and toppling directions of corresponding blocks can be discerned intuitively. Especially, by allocating the area of high dip maximum dip vector which can form the flanks of sliding block the potentiality for the formation of virtual sliding block has been estimated. Also, the potentiality of forming the triangular-sectioned sliding block has been determined by considering the dip angle of joint plane the dip direction of which is nearly opposite to that of the slope face. Safety factors of the different-shaped blocks of triangular section has been estimated and compared to the safety factor of the most hazardous block of rectangular section. For the wedge analysis the direction of crossline of two intersecting joint planes, which has same attribute of the maximum dip vector, is used so that wedge failures zone can be superimposed on the stereographic projection surface in which plane and toppling failure areas are already lineated. In addition the maximum dip vector zone of wedge top face has been delineated to extract the wedge top face-forming joint planes the orientation of which provides the vital information for the analysis of mechanical behavior of wedge block.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.109-112
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• 2001

This paper proposes an efficient algorithm of extracting anchor blocks for a semantic structure of a news video. We define the FRFD to calculate the frame difference of anchor face position rather than simply uses the general frame difference. Since, The FRFD value is sensitive to existing face in frame, anchor block can be efficiently extracted. In this paper, an algorithm to extract a face position using partial decoded MPEG data is also proposed. In this way a news video can be structured semantically using the extracted anchor blocks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1258-1268
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• 2004

We propose a new fast, algorithm which is used for detecting frontal face in the frequency domain based on human skin-color using OCT coefficient of dynamic image compression and skin color information. The region where each pixel has a value of skin-color were extracted from U and V value based on DCT coefficient obtained in the process of Image compression using skin-color map in the Y, U, V color space A morphological filter and labeling method are used to eliminate noise in the resulting image We propose the algorithm to detect fastly human face that estimate the directional feature and variance of luminance block of human skin-color Then Extraction of face was completed adaptively on both background have the object analogous to skin-color and background is simple in the proposed algorithm The performance of face detection algorithm is illustrated by some simulation results earned out on various races We confined that a success rate of 94 % was achieved from the experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2667-2682
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• 2015

Due to the wide application of face recognition (FR) in information security, surveillance, access control and others, it has received significantly increased attention from both the academic and industrial communities during the past several decades. However, partial face occlusion is one of the most challenging problems in face recognition issue. In this paper, a novel method based on linear regression-based classification (LRC) algorithm is proposed to address this problem. After all images are downsampled and divided into several blocks, we exploit the evaluator of each block to determine the clear blocks of the test face image by using linear regression technique. Then, the remained uncontaminated blocks are utilized to partial occluded face recognition issue. Furthermore, an improved Distance-based Evidence Fusion approach is proposed to decide in favor of the class with average value of corresponding minimum distance. Since this occlusion removing process uses a simple linear regression approach, the completely computational cost approximately equals to LRC and much lower than sparse representation-based classification (SRC) and extended-SRC (eSRC). Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and the performance is satisfactory. Through the comparison with the conventional methods (eigenface+NN, fisherfaces+NN) and the state-of-the-art methods (LRC, SRC and eSRC), the proposed method shows better performance and robustness.

• Journal of the Korean Geographical Society
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• v.28 no.2
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• pp.77-98
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• 1993

The intent of this study is to analyze the characteristics of distribution, patter, and deposits of the exposed debris slope landform by aerial photography interpretation, measure-ment on the topographical maps and field surveys in the southern part Taebaek mountains. It also aims to research the arrangement types of mountain slope and the landform development of debris slopes in this area. In conclusion, main observations can be summed up as follows. 1. The distribution characteristics 1)From the viewpoint of bedrocks, the distribution density of talus is high in case of the bedrock with high density of joints, sheeting structures and hard rocks, but that of the block stream is high in case of intrusive rocks with the talus line. 2)From the viewpoint of bedrocks, the distribution density of talus is high in case of the bedrock with high density of joints, sheeting structures and hard rocks, but that of the block stream is high in case of inrtusive rocks with the talus line. 2) From the viewpoint of distribution altitude, talus is mainly distributed in the 301~500 meters part above the sea level, while the block stream is distributed in the 101~300 meters part. 3) From the viewpoint of slope oriention, the distribution density of talus on the slope facing the south(S, SE, SW) is a little higher than that of talus on the slope facing the north(N, NE, NW). 2. The Pattern Characteristics 1) The tongue-shaped type among the four types is the most in number. 2) The average length of talus slope is 99 meters, especially that of talus composed of hornfels or granodiorite is longer. Foth the former is easy to make free face; the latter is easdy to produce round stones. The average length of block stream slope is 145 meters, the longest of all is one km(granodiorite). 3) The gradient of talus slope is 20~45${^\circ}$, most of them 26-30${^\croc}$; but talus composed of intrusive rocks is gentle. 4) The slope pattern of talus shows concave slope, which means readjustment of constituent debris. Some of the block stream slope patterns show concave slope at the upper slope and the lower slope, but convex slope at the middle slope; others have uneven slope. 3. The deposit characteristics 1) The average length of constituent debris is 48~172 centimeters in diameter, the sorting of debris is not bad without matrix. That of block stream is longer than that of talus; this difference of debris average diameter is funda-mentally caused by joint space of bedrocks. 2) The shape of constituent debris in talus is mainly angular, but that of the debris composed of intrusive rocks is sub-angular. The shape of constituent debris in block stream is mainly sub-roundl. 3) IN case dof talus, debris diameter is generally increasing with downward slope, but some of them are disordered and the debris diameter of the sides are larger than that of the middle part on a landform surface. In block stream, debris diameter variation is perpendicularly disordered, and the debris diameter of the middle part is generally larger than that of the sides on a landform surface. 4)The long axis orientation of debris is a not bad at the lower part of the slope in talus (only 2 of 6 talus). In block stream(2 of 3), one is good in sorting; another is not bad. The researcher thinks that the latter was caused by the collapse of constituent debris. 5) Most debris were weathered and some are secondly weathered in situ, but talus composed of fresh debris is developing. 4. The landform development of debris slopes and the arrangement types of the mountain slope 1) The formation and development period of talus is divided into two periods. The first period is formation period of talus9the last glacial period), the second period is adjustment period(postglacial age). And that of block stream is divided into three periods: the first period is production period of blocks(tertiary, interglacial period), the second formation period of block stream(the last glacial period), and the third adjustment period of block stream(postglacialage). 2) The arrangement types of mountain slope are divided into six types in this research area, which are as follows. Type I; high level convex slope-free face-talus-block stream-alluvial surface Type II: high level convex slope-free face-talus-alluvial surface Type III: free face-talus-block stream-all-uvial surface Type IV: free face-talus-alluval surface Type V: talus-alluval surface Type VI: block stream-alluvial surface Particularly, type IV id\s basic type of all; others are modified ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.