• Journal of Korea Multimedia Society
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• v.15 no.1
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• pp.40-50
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• 2012

Face recognition is a biometric technology used to identify individuals based on facial feature information. Previous studies of face recognition used features including the eye, mouth and nose; however, there have been few studies on the effects of using other facial components, such as the eyebrows and chin, on recognition performance. We measured the recognition accuracy affected by these facial components, and compared the differences between computer-based and human-based facial recognition methods. This research is novel in the following four ways compared to previous works. First, we measured the effect of components such as the eyebrows and chin. And the accuracy of computer-based face recognition was compared to human-based face recognition according to facial components. Second, for computer-based recognition, facial components were automatically detected using the Adaboost algorithm and active appearance model (AAM), and user authentication was achieved with the face recognition algorithm based on principal component analysis (PCA). Third, we experimentally proved that the number of facial features (when including eyebrows, eye, nose, mouth, and chin) had a greater impact on the accuracy of human-based face recognition, but consistent inclusion of some feature such as chin area had more influence on the accuracy of computer-based face recognition because a computer uses the pixel values of facial images in classifying faces. Fourth, we experimentally proved that the eyebrow feature enhanced the accuracy of computer-based face recognition. However, the problem of occlusion by hair should be solved in order to use the eyebrow feature for face recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.834-839
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• 2009

It is a key element that the problem of emotional feature extraction based on facial image to recognize a human emotion status. In this paper, we propose an Advanced AAM that is improved version of proposed Facial Expression Recognition Systems based on Bayesian Network by using FACS and AAM. This is a study about the most efficient method of optimal facial feature area for human emotion recognition about random user based on generalized HCI system environments. In order to perform such processes, we use a Statistical Shape Analysis at the normalized input image by using Advanced AAM and FACS as a facial expression and emotion status analysis program. And we study about the automatical emotional feature extraction about random user.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.117-124
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• 2013

In this paper, we present a new method which efficiently estimates a face direction from a sequences of input video images in real time fashion. For this work, the proposed method performs detecting the facial region and major facial features such as both eyes, nose and mouth by using the Haar-like feature, which is relatively not sensitive against light variation, from the detected facial area. Then, it becomes able to track the feature points from every frame using optical flow in real time fashion, and determine the direction of the face based on the feature points tracked. Further, in order to prevent the erroneously recognizing the false positions of the facial features when if the coordinates of the features are lost during the tracking by using optical flow, the proposed method determines the validity of locations of the facial features using the template matching of detected facial features in real time. Depending on the correlation rate of re-considering the detection of the features by the template matching, the face direction estimation process is divided into detecting the facial features again or tracking features while determining the direction of the face. The template matching initially saves the location information of 4 facial features such as the left and right eye, the end of nose and mouse in facial feature detection phase and reevaluated these information when the similarity measure between the stored information and the traced facial information by optical flow is exceed a certain level of threshold by detecting the new facial features from the input image. The proposed approach automatically combines the phase of detecting facial features and the phase of tracking features reciprocally and enables to estimate face pose stably in a real-time fashion. From the experiment, we can prove that the proposed method efficiently estimates face direction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1827-1835
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• 2010

In this paper, facial feature areas in user picture are created by facial feature points extracted by ASM(Active Shape Model). In a existing virtual make-up application, users manually select a few features that are exactly. Users are uncomfortable with this method. We propose a virtual makeup application using ASM that does not require user input. In order to express a natural makeup, the modified alpha blendings for each cosmetic are used to blend skin color with cosmetic color. The Virtual makeup application was implemented to apply Foundation, Blush, Lip Stick, Lip Liner, Eye Pencil, Eye Liner and Eye Shadow.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.714-720
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• 2004

In detecting human faces in color images, additional geometric computation is often necessary for validating the face-candidate regions having various forms. In this paper, we propose a method that detects the facial features using chrominance components of color which do not affected by face occlusion and orientation. The proposed algorithm uses the property that the Cb and Cr components have consistent differences around the facial features, especially eye-area. We designed the Mean-Gray Morphology operator to emphasize the feature areas in the eye-map image which generated by basic chrominance differences. Experimental results show that this method can detect the facial features under various face candidate regions effectively.

• Journal of Information Technology Applications and Management
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• v.10 no.4
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• pp.135-147
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• 2003

With the development of multimedia and optical technologies, application systems with facial features hare been increased the interests of researchers, recently. The previous research efforts in face processing mainly use the frontal images in order to recognize human face visually and to extract the facial expression. However, applications, such as image database systems which support queries based on the facial direction and image arrangement systems which place facial images automatically on digital albums, deal with the directional characteristics of a face. In this paper, we propose a method to detect facial directions by using facial features. In the proposed method, the facial trapezoid is defined by detecting points for eyes and a lower lip. Then, the facial direction formula, which calculates the right and left facial direction, is defined by the statistical data about the ratio of the right and left area in facial trapezoids. The proposed method can give an accurate estimate of horizontal rotation of a face within an error tolerance of $\pm1.31$ degree and takes an average execution time of 3.16 sec.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.487-492
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• 2008

Facial expression recognition is an intensive research area for designing Human Computer Interfaces. In this work, we present a new facial expression recognition system utilizing Enhanced Independent Component Analysis (EICA) for feature extraction and discrete Hidden Markov Model (HMM) for recognition. Our proposed approach for the first time deals with sequential images of emotion-specific facial data analyzed with EICA and recognized with HMM. Performance of our proposed system has been compared to the conventional approaches where Principal and Independent Component Analysis are utilized for feature extraction. Our preliminary results show that our proposed algorithm produces improved recognition rates in comparison to previous works.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.535-546
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• 2019

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.7-16
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• 2010

Face recognition of face images is an active subject in the area of computer pattern recognition, which has a wide range of potential. Automatic extraction of face image of the feature points is an important step during automatic face recognition. Whether correctly extract the facial feature has a direct influence to the face recognition. In this paper, a new method of facial feature extraction based on Discrete Wavelet Transform is proposed. Firstly, get the face image by using PC Camera. Secondly, decompose the face image using discrete wavelet transform. Finally, we use the horizontal direction, vertical direction projection method to extract the features of human face. According to the results of the features of human face, we can achieve face recognition. The result show that this method could extract feature points of human face quickly and accurately. This system not only can detect the face feature points with great accuracy, but also more robust than the tradition method to locate facial feature image.

• Science of Emotion and Sensibility
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• v.10 no.1
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• pp.113-125
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• 2007

The aim of the experimental studies described in this paper is to investigate the effects of the face/eye/mouth areas using dynamic facial expressions and static facial expressions on emotional recognition. Using seven-seconds-displays, experiment 1 for basic emotions and experiment 2 for complex emotions are executed. The results of two experiments supported that the effects of dynamic facial expressions are higher than static one on emotional recognition and indicated the higher emotional recognition effects of eye area on dynamic images than mouth area. These results suggest that dynamic properties should be considered in emotional study with facial expressions for not only basic emotions but also complex emotions. However, we should consider the properties of emotion because each emotion did not show the effects of dynamic image equally. Furthermore, this study let us know which facial area shows emotional states more correctly is according to the feature emotion.