• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.617-624
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• 2004

This paper describes a new facial feature localization method that uses Adjacent Depth Differences(ADD) in 3D facial surface. In general, human recognize the extent of deepness or shallowness of region relatively, in depth, by comparing the neighboring depth information among regions of an object. The larger the depth difference between regions shows, the easier one can recognize each region. Using this principal, facial feature extraction will be easier, more reliable and speedy. 3D range images are used as input images. And ADD are obtained by differencing two range values, which are separated at a distance coordinate, both in horizontal and vertical directions. ADD and input image are analyzed to extract facial features, then localized a nose region, which is the most prominent feature in 3D facial surface, effectively and accurately.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.875-882
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• 2008

Ear detection in an image processing is the one of the important area in biometrics. In this paper we propose a human ear detection algorithm with side face images. First, we search a face candidate area in an input image by using skin-color model and try to find an ear area based on Haar-like feature. Then, to verity whether it is the ear area or not, we use the template which is excellent object classification compare to recognize the characters in the plate. In this experiment, the proposed method showed that the processing speed is improved by 60% than previous works and the detection success rate is 92%.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.31-37
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• 2015

Symmetry is easily found in animals and plants as well as in artificial structures. It is useful not only for human cognitive process but also for image understanding by computer. Application areas include face detection and recognition, indexing of image database, image segmentation and detection, and analysis of medical images. The method used in this paper extracts edges, and the perpendicular bisector of any pair of selected edge points is considered to be a candidate axis of symmetry. The coefficients of the perpendicular bisectors are accumulated in the coefficient space. Axis of symmetry is determined to be the line for which the histogram has maximum value. This method shows good results, but the usefulness of the method is restricted because the amount of computation increases proportional to the square of the number of edges. In this paper, an acceleration method is proposed which performs $2^{2n}$ times faster than the original one. Experiment on 20 test images shows that the proposed method using level-3 image segmentation performs 63.9 times faster than the original method.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.539-554
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• 2018

Effective interaction between user and device is considered an important ability of IoT devices. For some applications, it is necessary to recognize human facial expressions in real time and make accurate judgments in order to respond to situations correctly. Therefore, many researches on facial image analysis have been preceded in order to construct a more accurate and faster recognition system. In this study, we constructed an automatic recognition system for facial expressions through two steps - a facial recognition step and a classification step. We compared various models with different sets of data with pixel information, landmark coordinates, Euclidean distances among landmark points, and arctangent angles. We found a fast and efficient prediction model with only 30 principal components of face landmark information. We applied several prediction models, that included linear discriminant analysis (LDA), random forests, support vector machine (SVM), and bagging; consequently, an SVM model gives the best result. The LDA model gives the second best prediction accuracy but it can fit and predict data faster than SVM and other methods. Finally, we compared our method to Microsoft Azure Emotion API and Convolution Neural Network (CNN). Our method gives a very competitive result.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.31-39
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• 2010

In some image processing system or the users who want to see a specific region of image simply, if a part of the image has higher quality than other regions, it would be a nice service. Specifically in mobile environments, preferential service was needed, as the screen size is small. So, JPEG2000 supplies this function. But this doesn't support the process to extract specific regions or service and does the functions to add some techniques. It is called by ROI(Region-of-Interest). In this paper, we use images including human faces, which are processed most preferentially and compressed with high quality. Before an image is served to the users, it is compressed and saved. Here, the face parts are compressed with higher quality than the background which are relatively with lower quality. This technique can offer better service with preferential transferring of the faces, too. Besides, whole regions of the image are compressed with same quality and after searching the faces, they can be preferentially transferred. In this paper, we use a face extraction approach based on neural network and the preferential processing with EBCOT of JPEG2000. For experimentation, we use images having several human faces and evaluate objectively and subjectively, and proved that this approach is a nice one.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2B
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• pp.168-175
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• 2011

In modem society, human relationships has been received much attention as important element to judge the success as well as the social life of the individual. To respond to these changing times has been used various ways to maintain an appropriate relationship that each other's character can be predicted. In this paper, we should be carried out a study on correlation analysis and features of five-character types to extract shape of philtrum, mouth, ears in facial image of Men in the 20's for Interview system application. From this, we extracted to area of philtrum, mouth, ears by Visual C++ to face and side image. Then we performed analysis, comparison a group of S-character types to find a result according to philtrum rate, mouth size, shape of ears. As a result, we drew a significance through morphological results by philtrum rate, mouth size, shape of ears as five-character types.

• Applied Microscopy
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• v.40 no.4
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• pp.267-270
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• 2010

The skin is the largest organ of the integument system whose surface is closely related with many physiological and pathological conditions. Various methods are used to understand the structural and functional status of human skin. We would like to present usefulness of scanning electron microscopic (SEM) observation of skin replica and its significance of training module for a novice. The silicon replicas from several regions of the body (hand, finger, forearm, lip, and face) were casted by applying Exafine$^{(R)}$ mixture. The positive replicas were prepared by applying EPON 812 mixture on negative silicon replicas. Some of the negative silicon replicas were cut with a razor blade and surface profiles were observed. The negative and positive replicas were coated with platinum and were observed under the scanning electron microscope. We could investigate the detailed structures of the human skin surface without any physical damage to the subject. The positive replicas depicted real surface structure of the human skin vividly. The cross sectional view of the negative silicon replicas provided surface profile clearly. The scanning electron microscopic observation of the human skin replicas would be useful to study skin surface structures and to evaluate medical and esthetical applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.374-376
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• 2018

In this study, we developed a mobile home robot using a face recognition method using a camera attached to a raspberry pie. It receives the real time image through the camera attached to the raspberry pie, recognizes the face of the person, and distinguishes the operation of the smart cool air temperature device according to the result. It is expected that the robot will be able to increase the energy utilization efficiency by allowing the robot to operate in cold and hot winds only where there is no human being, instead of operating the hot and cold air conditioner.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2370-2372
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• 1998

Recently we have developed an active stereo head-eye system with vergence, name it KIST HECter(Head-Eye System with Colored Stero Vision), based on the analysis of human's neck and eye motion at visual behavior. Our HECter is a five degree-of-freedom system composed of pan and tilt motion in neck part and independent vergence motion of binocular cameras and commonly shared elevation axis in eye part. And stereo vision Provides two color image, which are processed by powerful each TMS32080 vision board. The shape and size are designed to be almost same as human face. The ability to vergence has significant importance and gives many beneficial merits. On its mechanical implementation we adapt a non-parallelogram 4-bar linkage mechanism since it provides high accuracy in transfering motion and enables compact and flexible design.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.105-113
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• 2018

In this paper, we proposed a method for effective classification of eye, nose, and mouth of human face. Most recent image classification uses Convolutional Neural Network(CNN). However, the features extracted by CNN are not sufficient and the classification effect is not too high. We proposed a new algorithm to improve the classification effect. The proposed method can be roughly divided into three parts. First, the Haar feature extraction algorithm is used to construct the eye, nose, and mouth dataset of face. The second, the model extracts CNN features of image using AlexNet. Finally, Haar-CNN features are extracted by performing convolution after Haar feature extraction. After that, CNN features and Haar-CNN features are fused and classify images using softmax. Recognition rate using mixed features could be increased about 4% than CNN feature. Experiments have demonstrated the performance of the proposed algorithm.