• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.155-167
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• 2007

There are four types of human beings according to the Sasang Typology, Oriental medical doctors frequently prescribe healthcare information and treatment depending on one's type, The feature ratios (Table 1) on the human face are the most important criterions to decide which type a patient is. In this paper, we proposed a system to extract these feature ratios from the people's side face, There are two challenges in acquiring the feature ratio: one that selecting representative features; the other, that detecting region of interest from human profile facial image effectively and calculating the feature ratio accurately. In our system, an adaptive color model is used to separate human side face from background, and the method based on geometrical model is designed for region of interest detection. Then we present the error analysis caused by image variation in terms of image size and head pose, To verify the efficiency of the system proposed in this paper, several experiments are conducted using about 173 korean's left side facial photographs. Experiment results shows that the accuracy of our system is increased 17,99% after we combine the front face features with the side face features, instead of using the front face features only.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.631-653
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• 2020

Sign language is a natural, visually oriented and non-verbal communication channel between people that facilitates communication through facial/bodily expressions, postures and a set of gestures. It is basically used for communication with people who are deaf or hard of hearing. In order to understand such communication quickly and accurately, the design of a successful sign language translation system is considered in this paper. The proposed system includes object detection and classification stages. Firstly, Single Shot Multi Box Detection (SSD) architecture is utilized for hand detection, then a deep learning structure based on the Inception v3 plus Support Vector Machine (SVM) that combines feature extraction and classification stages is proposed to constructively translate the detected hand gestures. A sign language fingerspelling dataset is used for the design of the proposed model. The obtained results and comparative analysis demonstrate the efficiency of using the proposed hybrid structure in sign language translation.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.461-468
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• 2003

In a sequence of images obtained by surveillance cameras, facial regions appear very small and their colors change abruptly by lighting condition. This paper proposes a new face detection scheme, robust on complex background, small size, and lighting conditions. The proposed method is consisted of three processes. In the first step, the candidates for the face regions are selected using face color distribution and motion information. In the second stage, the non-face regions are removed using face color ratio, boundary ratio, and average of column-wise intensity variation in the candidates. The face regions containing eyes and mouth are segmented and classified, and then they are scored using their topological relations in the last step. To speed up and improve a performance the above process, a block based image segmentation technique is used. The experiments have shown that the proposed algorithm detects faced regions with more than 91% of accuracy and less than 4.3% of false alarm rate.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.141-156
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• 2013

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non-face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.542-548
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• 2006

In this paper, we propose a face region detection/verification method using wavelet packet analysis and structural statistic for frontal human color image. The method extracts skin color lesions from input images, first. and then applies spatial restrictive conditions to the region, and determines whether the region is face candidate region or not. In second step, we find eye region in the face candidate region using structural statistic for standard korean faces. And in last step, the face region is verified via wavelet packet analysis if the face torture were satisfied to normal texture conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.677-682
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• 2010

This paper addresses a method of estimating roughly the human pose by comparing Haar-wavelet value which is learned in face detection technology using AdaBoost algorithm. We also presents its application to face recognition. The learned weak classifier is used to a Haar-wavelet robust to each pose's feature by comparing the coefficients during the process of face detection. The Mahalanobis distance is used to measure the matching degree in Haar-wavelet selection. When a facial image is detected using the selected Haar-wavelet, the pose is estimated. The proposed pose estimation can be used to improve face recognition speed. Experiments are conducted to evaluate the performance of the proposed method for pose estimation.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.44 no.1
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• pp.18-24
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• 2018

Objectives: Foreign bodies (FBs) account for 3.8% of all pathologies of the head and neck region, and approximately one third of them are missed on initial examination. Thus, FBs represent diagnostic challenges to maxillofacial surgeons, rendering it necessary to employ an appropriate imaging modality in suspected cases. Materials and Methods: In this cross-sectional study, five different materials, including wood, metal, glass, tooth and stone, were prepared in three sizes (0.5, 1, and 2 mm) and placed in three locations (soft tissue, air-filled space and bone surface) within a sheep's head (one day after death) and scanned by panoramic radiography, cone-beam computed tomography (CBCT), and ultrasonography (US) devices. The images were reviewed, and accuracy of the detection modalities was recorded. The data were analyzed statistically using the Kruskal-Wallis, Mann-Whitney U-test, Friedman, Wilcoxon signed-rank and kappa tests (P<0.05). Results: CBCT was more accurate in detection of FBs than panoramic radiography and US (P<0.001). Metal was the most visible FB in all of modalities. US was the most accurate technique for detecting wooden materials, and CBCT was the best modality for detecting all other materials, regardless of size or location (P<0.05). The detection accuracy of US was greater in soft tissue, while both CBCT and panoramic radiography had minimal accuracy in detection of FBs in soft tissue. Conclusion: CBCT was the most accurate detection modality for all the sizes, locations and compositions of FBs, except for the wooden materials. Therefore, we recommend CBCT as the gold standard of imaging for detecting FBs in the maxillofacial region.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.311-318
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• 2013

For face detection techniques, the correctness of detection decreases with different lightings and backgrounds so such requires new methods and techniques. This study has aimed to obtain data for reasoning human emotional information by analyzing the components of the eyes and mouth that are critical in expressing emotions. To do this, existing problems in detecting face are addressed and a detection method that has a high detection rate and fast processing speed good at detecting environmental elements is proposed. This method must detect a specific part (eyes and a mouth) by using Haar-like Feature technique with the application of an integral image. After which, binaries detect elements based on color information, dividing the face zone and skin zone. To generate correct shape, the shape of detected elements is generated by using a bezier curve-a curve generation algorithm. To evaluate the performance of the proposed method, an experiment was conducted by using data in the Face Recognition Homepage. The result showed that Haar-like technique and bezier curve method were able to detect face elements more elaborately.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.85-92
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• 2020

Deep learning shows outstanding performance in image and video analysis, such as object classification, object detection and semantic segmentation. In this paper, it is analyzed that the performances of deep learning models can be affected by characteristics of train dataset. It is proposed as a method for selecting activation function and optimization algorithm of deep learning to classify facial expression. Classification performances are compared and analyzed by applying various algorithms of each component of deep learning model for CK+, MMI, and KDEF datasets. As results of simulation, it is shown that genetic algorithm can be an effective solution for optimizing components of deep learning model.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.944-947
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• 2022

Due to the coronavirus pandemic, the wearing of a mask has been increasing worldwide; thus, the importance of image analysis on masked face images has become essential. Although head pose estimation can be applied to various face-related applications including driver attention, face frontalization, and gaze detection, few studies have been conducted to address the performance degradation caused by masked faces. This study proposes a new data augmentation that synthesizes the masked face, depending on the face image size and poses, which shows robust performance on BIWI benchmark dataset regardless of mask-wearing. Since the proposed scheme is not limited to the specific model, it can be utilized in various head pose estimation models.