• ETRI Journal
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• v.33 no.2
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• pp.251-258
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• 2011

Applying face alignment after face detection exerts a heavy influence on face recognition. Many researchers have recently investigated face alignment using databases collected from images taken at close distances and with low magnification. However, in the cases of home-service robots, captured images generally are of low resolution and low quality. Therefore, previous face alignment research, such as eye detection, is not appropriate for robot environments. The main purpose of this paper is to provide a new and effective approach in the alignment of small and blurred faces. We propose a face alignment method using the confidence value of Real-AdaBoost with a modified census transform feature. We also evaluate the face recognition system to compare the proposed face alignment module with those of other systems. Experimental results show that the proposed method has a high recognition rate, higher than face alignment methods using a manually-marked eye position.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.15-22
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• 2019

There are many ways to perform face recognition. The first step of face recognition is the face detection step. If the face is not found in the first step, the face recognition fails. Face detection research has many difficulties because it can be varied according to face size change, left and right rotation and up and down rotation, side face and front face, facial expression, and light condition. In this study, facial features are extracted and the extracted features are geometrically reconstructed in order to improve face recognition rate in extracted face region. Also, it is aimed to adjust face angle using reconstructed facial feature vector, and to improve recognition rate for each face angle. In the recognition attempt using the result after the geometric reconstruction, both the up and down and the left and right facial angles have improved recognition performance.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1252-1255
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• 2002

In this paper, face recognition algorithm, by using skin color information of HSI color coordinate collected from face images, elliptical mask, fratures of face including eyes, nose and mouth, and geometrical feature vectors of face and facial angles, is proposed. The proposed algorithm improved face region extraction efficacy by using HSI information relatively similar to human's visual system along with color tone information about skin colors of face, elliptical mask and intensity information. Moreover, it improved face recognition efficacy with using feature information of eyes, nose and mouth, and Θ1(ACRED), Θ2(AMRED) and Θ 3(ANRED), which are geometrical face angles of face. In the proposed algorithm, it enables exact face reading by using color tone information, elliptical mask, brightness information and structural characteristic angle together, not like using only brightness information in existing algorithm. Moreover, it uses structural related value of characteristics and certain vectors together for the recognition method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2547-2567
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• 2021

It has been widely acknowledged that occlusion impairments adversely distress many face recognition algorithms' performance. Therefore, it is crucial to solving the problem of face image occlusion in face recognition. To solve the image occlusion problem in face recognition, this paper aims to automatically de-occlude the human face majority or discriminative regions to improve face recognition performance. To achieve this, we decompose the generative process into two key stages and employ a separate generative adversarial network (GAN)-based network in both stages. The first stage generates an initial coarse face image without an occlusion mask. The second stage refines the result from the first stage by forcing it closer to real face images or ground truth. To increase the performance and minimize the artifacts in the generated result, a new refine loss (e.g., reconstruction loss, perceptual loss, and adversarial loss) is used to determine all differences between the generated de-occluded face image and ground truth. Furthermore, we build occluded face images and corresponding occlusion-free face images dataset. We trained our model on this new dataset and later tested it on real-world face images. The experiment results (qualitative and quantitative) and the comparative study confirm the robustness and effectiveness of the proposed work in removing challenging occlusion masks with various structures, sizes, shapes, types, and positions.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.647-650
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• 2021

The most powerful and modern face recognition techniques are using deep learning methods that have provided impressive performance. The outbreak of COVID-19 pneumonia has spread worldwide, and people have begun to wear a face mask to prevent the spread of the virus, which has led existing face recognition methods to fail to identify people. Mainly, it pushes masked face recognition has become one of the most challenging problems in the face recognition domain. However, deep learning methods require numerous data samples, and it is challenging to find benchmarks of masked face datasets available to the public. In this work, we develop a new simulated masked face dataset that we can use for masked face recognition tasks. To evaluate the usability of the proposed dataset, we also retrained the dataset with ArcFace based system, which is one the most popular state-of-the-art face recognition methods.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.105-108
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• 2018

Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.817-828
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• 2004

In this paper, we propose vector-based face generation system that uses montage and shading method and preserves designer(artist)'s style. Proposed system generates character's face similar to human face automatically using facial features that extracted from a photograph. In addition, unlike previous face generation system that uses contours, we propose the system is based on color and composes face from facial features and shade extracted from a photograph. Thus, it has advantages that can make more realistic face similar to human face. Since this system is vector-based, the generated character's face has no size limit and constraint. Therefore it is available to transform the shape freely and to apply various facial expressions to 2D face. Moreover, it has distinctiveness with another approaches in point that can keep artist's impression just as it is in result.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.91-98
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• 2019

Many researchers have attempted to recognize three-dimensional faces using feature points extracted from two-dimensional facial photographs. However, due to the limit of flat photographs, it is very difficult to recognize faces rotated more than 15 degrees from original feature points extracted from the photographs. As such, it is difficult to create an algorithm to recognize faces in multiple angles. In this paper, it is proposed a new algorithm to recognize three-dimensional face recognition based on feature points extracted from a flat photograph. This method divides into six feature point vector zones on the face. Then, the vector value is compressed and expanded according to the rotation angle of the face to recognize the feature points of the face in a three-dimensional form. For this purpose, the average of the compressibility and the expansion rate of the face data of 100 persons by angle and face zone were obtained, and the face angle was estimated by calculating the distance between the middle of the forehead and the tail of the eye. As a result, very improved recognition performance was obtained at 30 degrees of rotated face angle.

• Journal of Broadcast Engineering
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• v.9 no.2
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• pp.164-173
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• 2004

In this paper, we propose a system that can analyze appearance interval of casts by detecting and recognizing casts in broadcasting videos. The cast is one of the most important characteristics in broadcasting videos such as drama and sports. In this paper, we propose the ACAV(Automatic Cast-list Analysis in Videos) system that analyzes cast-list automatically in video. The ACAV system consists of FAGIS(FAce reGIStration) which registers detected faces into the face DB and FACOG(FAce reCOGnition) that analyses the cast-list in video sequence using the face DB. We evaluate performance of the ACAV system by comparing with FaceIt, one of the most well-known commercial systems for the cast-list analysis. The ACAV shows face detection and recognition rates of 84.3% and 75.7% that are about 30% and 27.5% higher than those of FaceIt, respectively. The ACAV system can be applied to mass broadcasting videos management system for broadcasters and video management system of PVR(Personal Video Recorder) and mobile phone for the public.

• Journal of Distribution Science
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• v.18 no.10
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• pp.39-52
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• 2020

Purpose: This study aims to find out the relationship between the impact of Korean crew on airline service quality in the global aviation market, which is the representative of the face-to-face sales and can help in the face-to-face market of aviation services. Research design, data: The survey was conducted for about a month from March 1 to April 6, 2020, and a total of 300 copies were used in the analysis of the results. To verify the hypothesis, data was analyzed using the statistical package program SPSS 18.0, and frequency analysis, exploratory factor analysis, correlation analysis, and multiple regression analysis were performed. Results: It is a study on the sales of face-to-face service of crews of global airlines. Non-verbal elements in airline service face-to-face sales have been shown to have a significant impact on service quality. Conclusions: In the face-to-face service sales of global airlines, communication has been shown to affect service quality. A face-to-face sale using mother tongue means important. The better the flight attendant's linguistic face-to-face selling ability, the more positive on the airline's quality of service. It suggests that the communication skills of managers in the aviation service market are important for repurchase.