• Smart Media Journal
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• v.5 no.1
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• pp.24-29
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• 2016

To satisfy the needs of high-level intelligent surveillance system, it shall be able to extract objects and classify to identify precise information on the object. The representative method to identify one's identity is face recognition that is caused a change in the recognition rate according to environmental factors such as illumination, background and angle of camera. In this paper, we analyze the robust face recognition of face image by changing the distance through a variety of experiments. The experiment was conducted by real face images of 1m to 5m. The method of face recognition based on Linear Discriminant Analysis show the best performance in average 75.4% when a large number of face images per one person is used for training. However, face recognition based on Convolution Neural Network show the best performance in average 69.8% when the number of face images per one person is less than five. In addition, rate of low resolution face recognition decrease rapidly when the size of the face image is smaller than $15{\times}15$.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.227-238
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• 2022

This study is the case of non-face-to-face teaching-learning in the subject of "Research and Guidance on Early Childhood Materials" in the pre-child teacher training program. The study conducted a non-face-to-face teaching-learning model for 18 students at B University in region C who took lectures on 'Research and Guidance on Early Childhood Materials' in the first semester of 2021. As a non-face-to-face teaching-learning model, it consisted of video lectures, real-time zoom classes, and various forms of 'communication' through frequent feedback and interaction and 'participation'. As a teaching-learning strategy for the participation of pre-service early childhood teachers, comment on questions related to early childhood materials, in-depth reflection on early childhood materials through writing reflective journals and observation reports, and step-by-step presentation of making childhood materials plans, processes, and results were carried out. As a result of exploring the experience of making early childhood materials for pre-service early childhood teachers, factors such as "growth experience through trial and error," "thinking from child's point of view", "Increase efficiency and reduce burden through communication", "Process rather than result" and "The importance of communication and interaction in non-face-to-face classes"

• Journal of Dental Rehabilitation and Applied Science
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• v.19 no.4
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• pp.297-308
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• 2003

Face-bow is used to transfer models to the articulator in diagnosing the patient or treating problems associated with occlusion. However, there have been few reports on the reliability of the face-bow procedure and the relationship between the experience of the operator and the reliability of the face-bow procedure. The purposes of this study are to examine the reliability of the face-bow procedure and to evaluate whether the face-bow transferring has any training effect. Nine dentists working at M hospital conducted a face-bow transfer in one patient having a normal dentition and interdental relationship. The procedure was done two times a week for four weeks. The maxillary model was mounted to the articulator every time, then the landmarks on the maxillary right first molar, the maxillary left central incisor, and the maxillary left first molar were measured with a special three-dimensional instrument. These data were input into a computer, and evaluated statistically. The results were as follows ; 1. When examined with ANOVA test, the results were p=0.2040 in maxillary right first molar, p=0.0578 in maxillary left incisor, and p=0.1433 in maxillary left first molar. There was no significant(0< $p{\leq}0.05$). 2. Training 1) The correlation coefficient between trial and rejection was -0.578 when analyzed with T-distribution. The more we tried, the less errors we found. 2) When the S.D. of the first three trials was compared to the S.D. of the last three trials in face-bow transfer, the results showed that the former was larger than the latter in thirty-nine times, and the latter was larger than the former in fifteen times. The more we tried face-bow transfer, the less errors we found. 3. When the S.D. of x, y, z coordinates were examined, the S.D. of x coordinates had the largest measurement in five times, the S.D. of y coordinates had the largest measurement in four times, and the S.D. of z coordinates had the largest measurement in nine times. The possibility which the error can occur in z coordinate was the highest.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.647-650
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.19-32
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• 2007

This paper present a new face recognition method based on LVQ neural net to construct a real time face recognition system. The previous researches which used PCA, LDA combined neural net usually need much time in training neural net. The supervised LVQ neural net needs much less time in training and can maximize the separability between the classes. In this paper, the proposed method transforms the input face image by PCA and LDA sequentially into low-dimension feature vectors and recognizes the face through LVQ neural net. In order to make the system robust to external light variation, light compensation is performed on the detected face by max-min normalization method as preprocessing. PCA and LDA transformations are applied to the normalized face image to produce low-level feature vectors of the image. In order to determine the initial centers of LVQ and speed up the convergency of the LVQ neural net, the K-Means clustering algorithm is adopted. Subsequently, the class representative vectors can be produced by LVQ2 training using initial center vectors. The face recognition is achieved by using the euclidean distance measure between the center vector of classes and the feature vector of input image. From the experiments, we can prove that the proposed method is more effective in the recognition ratio for the cases of still images from ORL database and sequential images rather than using conventional PCA of a hybrid method with PCA and LDA.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.44-51
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• 2009

This paper addresses a face verification method using the nonlinear composite filter. This face verification process can be simple and speedy because it does not require any reprocessing such as face detection, alignment or cropping. The optimum nonlinear composite filter is derived by minimizing the output energy due to additive noise and an input scene while maintaining the outputs of training images constant. The filter is equipped with the discrimination capability and the robustness to additive noise by minimizing the outputs of the input scene and the noise, respectively. We build the nonlinear composite filter with two training images and compare the filter with the conventional synthetic discriminant function (SDF) filter. The receiver operating characteristics (ROC) curves are presented as a metric for the performance evaluation. According to the experimental results the optimum nonlinear composite filter is shown to be a robust scheme for face verification in low resolution and noise environments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.112-120
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• 2009

Face membership authentication is to decide whether an incoming person is an enrolled member or not using face recognition, and basically belongs to two-class classification where support vector machine (SVM) has been successfully applied. The previous SVMs used for face membership authentication have been trained and tested using image feature vectors extracted from member face images of each class (enrolled class and unenrolled class). The SVM so trained using image feature vectors extracted from members in the training set may not achieve robust performance in the testing environments where configuration and size of each class can change dynamically due to member's joining or withdrawal as well as where testing face images have different illumination, pose, or facial expression from those in the training set. In this paper, we propose an effective class discriminating feature vector-based SVM for robust face membership authentication. The adopted features for training and testing the proposed SVM are chosen so as to reflect the capability of discriminating well between the enrolled class and the unenrolled class. Thus, the proposed SVM trained by the adopted class discriminating feature vectors is less affected by the change in membership and variations in illumination, pose, and facial expression of face images. Through experiments, it is shown that the face membership authentication method based on the proposed SVM performs better than the conventional SVM-based authentication methods and is relatively robust to the change in the enrolled class configuration.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.465-472
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• 2022

In this paper, the style synthesis network is trained to generate style-synthesized video through the style synthesis through training Stylegan and the video synthesis network for video synthesis. In order to improve the point that the gaze or expression does not transfer stably, 3D face restoration technology is applied to control important features such as the pose, gaze, and expression of the head using 3D face information. In addition, by training the discriminators for the dynamics, mouth shape, image, and gaze of the Head2head network, it is possible to create a stable style synthesis video that maintains more probabilities and consistency. Using the FaceForensic dataset and the MetFace dataset, it was confirmed that the performance was increased by converting one video into another video while maintaining the consistent movement of the target face, and generating natural data through video synthesis using 3D face information from the source video's face.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.