• Journal of Electrical Engineering and Technology
• /
• v.12 no.1
• /
• pp.402-410
• /
• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• Proceedings of the IEEK Conference
• /
• 2000.11c
• /
• pp.113-116
• /
• 2000

Human face recognition and 3D human face reconstruction has been studied in this paper. To find the facial feature points, find edge from input image and analysis the accumulated histogram of edge information. This paper use a Generic Face Model to display the 3D human face model which was implement with OpenGL and generated with 500 polygons. For reality of 3D human face model, we propose Group matching mapping method between facial feature points and the one of Generic Face Model. The personalized 3D human face model which resembles real human face can be generated automatically in less than 5 seconds on Pentium PC.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2016-2019
• /
• 2003

In this paper, we take some features for face recognition out of face image, using a simple type of templates. We use the extracted features to do Adaboost learning for face recognition. Using a carefully-chosen feature among these features, we can make a weak face classifier for face recognition. And doing Adaboost learning on and on with those chosen several weak classifiers, we can get a strong face classifier. By using Adaboost Loaming, we can choose particular features which is not easily subject to changes in illumination and facial expression about several images of one person, and construct face recognition system. Therefore, the face classifier bulit like the above way has robustness in both facial expression and illumination variation, and it finally gives capability of recognizing face fast due to the simple feature.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.169.2-169
• /
• 2001

In this paper, we propose the face recognition system using the neural network. A difficult procedure in constructing the entire recognition systems is the feature extraction from the face imga. And a key poing is the design of the matching function that relates the set of feature values to the appropriate face candidates. We use the length and angle values as feature values that are extracted from the face image normalized to the range of [0,1]. These features values are applied to the input layer of the neural network. Then, these multi-layered perceptron learns or gives otput result. By using the neural network we need not to design the matching function. This function may have nonlinear attributes considerably and would be ...

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.12
• /
• pp.4835-4848
• /
• 2020

The recent expansion of the digital content market is increasing the technical demand for various facial image transformations within the virtual environment. The recent image translation technology enables changes between various domains. However, current image-to-image translation techniques do not provide stable performance through unsupervised learning, especially for shape learning in the face transition field. This is because the face is a highly sensitive feature, and the quality of the resulting image is significantly affected, especially if the transitions in the eyes, nose, and mouth are not effectively performed. We herein propose a new unsupervised method that can transform an in-wild face image into another face style through radical transformation. Specifically, the proposed method applies two face-specific feature loss functions for a generative adversarial network. The proposed technique shows that stable domain conversion to other domains is possible while maintaining the image characteristics in the eyes, nose, and mouth.

• Journal of the Korean Society of Radiology
• /
• v.10 no.8
• /
• pp.571-576
• /
• 2016

3D printing has recently been used in various fields. Among various applications, 3D face data must be generated for 3D face printing. A laser scanner is used to acquire 3D face data, but there is a restriction that a person should not move during scanning. In this paper, we propose a 3D face modeling method based on a single image and a face transformation system to use the generated 3D face for virtual cosmetic surgery. We have defined facial feature points from the 3D face database for 3D face data generation. After extracting feature points from a single face image, 3D face of the input face image is generated corresponding to the 3D face feature points defined from the 3D face database. After 3D face modeling, 3D face modification part is applied for use such as virtual cosmetic surgery.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.44 no.1
• /
• pp.102-112
• /
• 2007

This paper includes an analysis of face recognition algorithm to design hardware and presents fixed point model in accordance with it. Face recognition algorithm detects the positions of face and eyes to make use of their feature data to detect and verify human faces. It distinguishes a particular user by means of comparing them with registered face features. To implement the face recognition algorithm into hardware, we developed its fixed point model by analyzing face feature parameters, face acquisition data, and feature detection parameters and operation structure.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.1
• /
• pp.50-57
• /
• 2005

We present a simple and effective method of face and facial feature detection under pose variation of user face in complex background for the human-robot interaction. Our approach is a flexible method that can be performed in both color and gray facial image and is also feasible for detecting facial features in quasi real-time. Based on the characteristics of the intensity of neighborhood area of facial features, new directional template for facial feature is defined. From applying this template to input facial image, novel edge-like blob map (EBM) with multiple intensity strengths is constructed. Regardless of color information of input image, using this map and conditions for facial characteristics, we show that the locations of face and its features - i.e., two eyes and a mouth-can be successfully estimated. Without the information of facial area boundary, final candidate face region is determined by both obtained locations of facial features and weighted correlation values with standard facial templates. Experimental results from many color images and well-known gray level face database images authorize the usefulness of proposed algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.203-207
• /
• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

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