• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.3
• /
• pp.250-255
• /
• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.2
• /
• pp.252-258
• /
• 2007

In this paper, a face recognition algorithm system using Principal Component Analysis (PCA) is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals of Intelligent Control Laboratory (ICONL) face database. Simulations are carried out to investigate the algorithm recognition performance, which classified the face as a face or non-face and then classified it as known or unknown one. Particularly, a Principal Components of Linear Discriminant Analysis (PCA + LDA) face recognition algorithm is also proposed in order to confirm the recognition performances and the adaptability of a proposed PCA for a certain specific system.

• Proceedings of the KIEE Conference
• /
• 2008.10b
• /
• pp.333-334
• /
• 2008

A real-time face tracking is a broad topic, covering a large spectrum of technologies and applications. Briefly face tracking is a kind of tracing technique which follows human face in any directions. It needs some algorithms such as human face detection and motion controller to track face. Moreover, both processing time and calculation time are the most important factors that influence to drive tracking system. In this paper, two algorithms are used to find human face: earn-shift algorithm and face detection algorithm using OpenCV. Fuzzy controller is utilized to move pan-tilt camera system which can move four directions along to x-y axis.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.521-524
• /
• 2003

In this paper, we propose a face region extraction algorithm using color information and projection. After the extraction of face candidate image using adaptive color information, we project it into vertical direction to estimate the width of the face. Then the redundant parts of the face are efficiently removed by using the estimated face width. And the width information of the face is used at the horizontal projection step to extract the height of the face, and non-face region such as the neck and some background regions, which are represented as the similar skin color, effectively eliminated. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.267-268
• /
• 2006

Advanced TV makes the life more convenient for the viewers and it is based on the recognition technology. In this paper, we propose the implementation of face tracking system for efficient face recognition in intelligent digital TV. To recognize the face, face detection should be performed earlier. We use the motion information to track the face. Continuous face tracking is possible by using continuous detected face region and motion information. Thus the computational complexity of the recognition module in the whole system can be reduced.

• International Journal of Advanced Culture Technology
• /
• v.10 no.1
• /
• pp.294-301
• /
• 2022

COVID-19 is a crisis with numerous casualties. The World Health Organization (WHO) has declared the use of masks as an essential safety measure during the COVID-19 pandemic. Therefore, whether or not to wear a mask is an important issue when entering and exiting public places and institutions. However, this makes face recognition a very difficult task because certain parts of the face are hidden. As a result, face identification and identity verification in the access system became difficult. In this paper, we propose a system that can detect masked face using transfer learning of Yolov5s and recognize the user using transfer learning of Facenet. Transfer learning preforms by changing the learning rate, epoch, and batch size, their results are evaluated, and the best model is selected as representative model. It has been confirmed that the proposed model is good at detecting masked face and masked face recognition.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.10
• /
• pp.102-112
• /
• 1995

Temperature distribution on the rake face and flank face in orthogonal turning with cutting tool of high speed steel is studied by using a finite element method and experiments. Experiments are carried out to verify the validity of the temperature measurement by using a thermoelectric couple junction imbedded in a cutting tool of high speed steel. Good agreement is obtained between the analytical results and the experimental ones for the temperature distributions on both the rake face and flank face of cutting tool with high speed steel. The analytical results show that the temperature on the top flank face of a tool is higher than it on the top rake face of the tool because of the difference of the friction velocity on each face of the tool.

• Journal of Science Education
• /
• v.46 no.1
• /
• pp.40-52
• /
• 2022

Daily science classes, which have been continued as part of the spread of participatory science culture, has taken the lead in popularizing science as an effective out-of-school experiential and research activity. However, due to the recent COVID-19 situation, daily science classes have also become an environment in which there is no choice but to switch to non-face-to-face or to combine face-to-face and non-face-to-face education. Therefore, in this study, we examine how elementary school students participating in the non-face-to-face daily science class program change their usual fields of interest, perception of science and technology, interest about science, and scientific competency. In addition, the educational effectiveness of the non-face-to-face daily science class improved by comparing the differences in perceptions of students and parents, and future operation plans were sought. As a result of the study, after participating in the non-face-to-face daily science class program, students' interest in science and technology development, future technology, environmental pollution, and social media increased, and their interest in games decreased. Also, students' interest in science and technology activities, interest in science, and scientific competency also increased. This shows that non-face-to-face daily science class education is effective. Therefore, it was suggested that it is necessary to diversify the learning topics and content levels of the daily science class program, to expand the opportunities of non-face-to-face science education for underprivileged learners, and to develop and share science content using the latest media.

• Journal of Korea Multimedia Society
• /
• v.13 no.4
• /
• pp.487-495
• /
• 2010

It has been required to develop an accurate and reliable evaluation method for the performance of biometric systems as their use is getting popular. Among a number of biometric systems, face recognition is one of the most widely used techniques and this leads to develop a stable evaluation method for face recognition systems in order to standardize the performance of face recognition systems. However, it is considered as a difficult task to evaluation such systems due to a large number of factors that affect their performance. Thus, it may be infeasible to take into account all the environmental factors that are related to the performance of face recognition systems and this naturally suggests an evaluation method for the overall performance based on scenarios. In this paper, we have analyzed environmental factors that are related to the performance of general face recognition systems and proposed their evaluation method taking into account those factors. We have proposed an evaluation method based on scenario that considers the combination of individual environment factors instead of evaluating the performance of face recognition systems regarding each factor. Indeed, we have presented examples on the evaluation of face recognition systems based on scenario that takes into account overall environmental factors.

• The KIPS Transactions:PartB
• /
• v.17B no.5
• /
• pp.355-362
• /
• 2010

Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.