• International journal of advanced smart convergence
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• v.9 no.4
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• pp.192-197
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• 2020

In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

• Fashion & Textile Research Journal
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• v.7 no.5
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• pp.527-534
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• 2005

The purpose of this research is to study face images according to color of make-up was made by computer graphic simulation. The various facial images can be helpful for choosing suitable make-up color planning. In order to find out the differences of face images by make-up color, three different foundations and seven eye-shadows, six lips were applied on the round face model. Make-up Image Scale was used the scale of seven point modified the S-D method. Data were analyzed by Varimax perpendicular rotation method, Duncan's Multiple Range Test, Three-way ANOVA. As the result of make-up image perception analysis, a factor structure was divided into mildness, modernness, elegance, unique. The factor of mildness, modernness, unique affected on the foundation color. Foundation color was found out to be influential variable to distinguish color perception abilities. Also, the foundation, eye-shadow, lip color were influenced interactively on the perception of elegance factor. Pink color was important color, influenced on the mildness factor. Gray and purple color were influenced on the modernness factor. Mildness factor was perceived as the most bright foundation but unique factor was perceived as the most dark foundation. Then, the foundation, eye-shadow, lip color were influenced interactively on the perception of facial images. The results can be effectively applied to today's marketing and color design management which is focused on the product's emotional image in customer's mind.

• Korean Journal of Human Ecology
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• v.14 no.1
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• pp.143-153
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• 2005

The purpose of this research is to study face images according to the difference of facial shape and make-up. A variety of face images can be formulated by computer graphic simulation, combining numerously different facial shapes and make-up styles. In order to check out the diverse images by make-up styles, we applied five forms of eye brows, two types of eye shadows, and three lip shapes to the round-shaped face of a model. The question sheet, used with a operational stimulant in the experiment, contained 28 articles, composed of a pair of bi-ended adjective in 7 point scale. Data were analyzed using Varimax perpendicular rotation method, Duncan's Multiple Range Test, and Three-way ANOVA. After comparing various results of make-up application to various face types, we could find that facial shape, eye-brows, eye-shadow, and lip shapes influence interactively on total facial images. As a result of make-up image perception analyses, a factor structure was divided into mildness, modernness, elegance, and sociableness. Speaking of make-up image in terms of those factors, round form make-up style showed the highest level of mildness. Upward and straight style of make-up had the highest of modernness. Elegance level went highest when eye shadow style was round form and lip style was straight. Lastly, an incurve lip make-up style showed the highest of sociableness.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.4
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• pp.607-616
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• 2014

Face recognition is a big challenge in surveillance system since different rotation angles of the face make the difficulty to recognize the face of the same person. This paper proposes a novel method to recognize face with different head poses by using 3D information of the face. Firstly, head pose estimation (estimation of different head pose angles) is accomplished by the POSIT algorithm. Then, 3D face image data is constructed by using head pose estimation. After that, 2D image and the constructed 3D face matching is performed. Face verification is accomplished by using commercial face recognition SDK. Performance evaluation of the proposed method indicates that the error range of head pose estimation is below 10 degree and the matching rate is about 95%.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.4
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• pp.189-203
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• 2014

In this study, Creative motifs using face composition of Jogakbo and Knot Symbol were developed, and applied to traditional Baeja of Joseon Dynasty to develop design contents of traditional clothes. As for study method, 7 motifs of new formative image that integrates traditional beauty and contemporary sense were developed by applying Knot Symbols and face compositions of Jogakbo with the use of Adobe Illustrator CS6 and Adobe Photoshop CS6 vector graphic software. The motifs were designed in contemporary image in face compositions like rectangle pattern, triangle pattern, dual rectangle pattern, vertical and horizontal pattern, pinwheel pattern, gojunmun pattern and free pattern by involving various changes like repetition, rotation, reduction, expansion and decomposition and using the colors used in the Jogakbo. It is desired that through this study, traditional Baeja may develop to bear traditional and contemporary image so that our traditional clothes design may become global. Also it is anticipated that this study will contribute to development of culture products of Hanbok like Jeogori, pants and skirt that require change of design in the global era while maintaining traditional beauty to appeal to the emotions of world citizens.

• Journal of the Korea Computer Industry Society
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• v.8 no.3
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• pp.155-166
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• 2007

In this paper, captured face-image was pre-processing, segmentation, and extracting features from thinning by differential operator and minute-delineation. A straight line in slope-intercept form was transformed at the $r-\theta$ domain using Hough Transform, instead of Housdorff distance are extract feature as length, rotation, displacement of lines from thinning line components by differentiation. This research proposed a new approach compare with Hough Transformation and Housdorff Distance for face recognition so that Hough transform is simple and fast processing of face recognition than processing by Housdorff Distance. Rcognition accuracy rate is that Housdorff method is higher than Hough transformation's method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.983-991
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• 2005

Human faces are commonly found in a video such as a drama and provide useful information for video content analysis. Therefore, face detection plays an important role in applications such as face recognition, and face image database management. In this paper, we propose a face detection algorithm based on pre-processing of scene change detection for indexing and cast searching in video. The proposed algorithm consists of three stages: scene change detection stage, face region detection stage, and eyes and mouth detection stage. Experimental results show that the proposed algorithm can detect faces successfully over a wide range of facial variations in scale, rotation, pose, and position, and the performance is improved by $24\%$with profile images comparing with conventional methods using color components.

• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.179-186
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• 2007

This paper proposes a novel method for face detection method using Zernike moments. To detect the faces in an image, local regions in multiscale sliding windows are classified into face and non-face by a neural network, and input features of the neural network consist of Zernike moments. Feature dimension is reduced as the reconstruction capability of orthogonal moment. In addition, because the magnitude of Zernike moment is invariant to rotation, a tilted human face can be detected. Even so the detection rate of the proposed method about head on face is less than experiments using intensity features, the result of our method about rotated faces is more robust. If the additional compensation and features are utilized, the proposed scheme may be best suited for the later stage of classification.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.143-153
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• 2006

Facial feature detection is a basic technology in applications such as human computer interface, face recognition, face tracking and image database management. The speed of feature detection algorithm is one of the main issues for facial feature detection in real-time environment. Primary factors like a variation by lighting effect, location, rotation and complex background give an effect to decrease a detection ratio. A facial feature detection algorithm is proposed to improve the detection ratio and the detection speed. The proposed algorithm detects skin regions over the entire image improved by CLAHE, an algorithm for light compensation against varying lighting conditions. To extract facial feature points on detected skin regions, it uses appearance-based geometrical characteristics of a face. Since the method shows fast detection speed as well as efficient face-detection ratio, it can be applied in real-time application to face tracking and face recognition.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.372-378
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• 2003

Limitations on the linear discriminant analysis (LDA) for face rerognition, such as the loss of generalization and the computational infeasibility, are addressed and illustrated for a small number of samples. The principal component analysis (PCA) followed by the LDA mapping may be an alternative that ran overcome these limitations. We also show that any schemes based on either mappings or template matching are vulnerable to image variations due to rotation, translation, facial expressions, or local illumination conditions. This entails the importance of a proper preprocessing that can compensate for such variations. A simple template matching, when combined with the geometrically correlated feature-based detection as a preprocessing, is shown to outperform mapping techniques in terms of both the accuracy and the robustness to image variations.