• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.175-178
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• 2001

This paper proposes a novel human-computer interaction system for the disabled using recognition of face direction. Face direction is recognized by comparing positions of center of gravity between face region and facial features such as eyes and eyebrows. The face region is first selected by using color information, and then the facial features are extracted by applying a separation filter to the face region. The process speed for recognition of face direction is 6.57frame/sec with a success rate of 92.9% without any special hardware for image processing. We implement human-computer interaction system using screen menu, and show a validity of the proposed method from experimental results.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.19-28
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• 2017

Various methods for reducing the load on the server have been implemented in performing face recognition remotely by the spread of IP security cameras. In this paper, IP surveillance cameras at remote sites are input through a DSP board equipped with face detection function, and then face detection is performed. Then, the facial region image is transmitted to the server, and the face recognition processing is performed through face recognition distributed processing. As a result, the overall server system load and significantly reduce processing and real-time face recognition has the advantage that you can perform while linked up to 256 cameras. The technology that can accomplish this is to perform 64-channel face recognition per server using distributed processing server technology and to process face search results through 250 camera channels when operating four distributed processing servers there was.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2327-2330
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• 2003

This paper deals with face detection and recognition using ellipsodal information and wavelet packet analysis. We proposed two methods. First, Face detection method uses general ellipsodal information of human face contour and we find eye position on wavelet transformed face images A novel method for recognition of views of human faces under roughly constant illumination is presented. Second, The proposed Face recognition scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtered images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, the Euclidian distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the MIT FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method. The proposed system achieved an rate of 97％(MIT data), 95.8％(FERET databace)

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.61-74
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• 2012

In this paper, we propose a illumination-robust face recognition system using MCS-LBP and 2D-PCA algorithm. A binary pattern transform which has been used in the field of the face recognition and facial expression, has a characteristic of robust to illumination. Thus, this paper propose MCS-LBP which is more robust to illumination than previous LBP, and face recognition system fusing 2D-PCA algorithm. The performance evaluation of proposed system was performed by using various binary pattern images and well-known face recognition features such as PCA, LDA, 2D-PCA and ULBP histogram of gabor images. In the process of performance evaluation, we used a YaleB face database, an extended YaleB face database, and a CMU-PIE face database that are constructed under varying lighting condition, and the proposed system which consists of MCS-LBP image and 2D-PCA feature show the best recognition accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.143-147
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• 2012

The rapid growth of camera technology can provide various types of information which was not previously provided. Furthermore, IP camera which has rapid data transfer rate and high resolution particularly provide a lot of useful functions beyond the existing simple surveillance capabilities. We are developing Real-Time Face Recognition Access Control System based on the camera technology, and improvement of face detection and recognition algorithms are vitally needed to realize that system. In this paper, we proposes a method to improve the computing speed and detection rate by adding new features to the existing Viola-Jones detection algorithm.

• ETRI Journal
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• v.33 no.2
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• pp.251-258
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• 2011

Applying face alignment after face detection exerts a heavy influence on face recognition. Many researchers have recently investigated face alignment using databases collected from images taken at close distances and with low magnification. However, in the cases of home-service robots, captured images generally are of low resolution and low quality. Therefore, previous face alignment research, such as eye detection, is not appropriate for robot environments. The main purpose of this paper is to provide a new and effective approach in the alignment of small and blurred faces. We propose a face alignment method using the confidence value of Real-AdaBoost with a modified census transform feature. We also evaluate the face recognition system to compare the proposed face alignment module with those of other systems. Experimental results show that the proposed method has a high recognition rate, higher than face alignment methods using a manually-marked eye position.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.193-205
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• 2003

In this paper, we suggest user authentication and authorization system for internet banking by face recognition. The system is one of Biometrics technology to verify and authorize personnel identification and is more unobtrusive than the other technologies, because they use physiological characteristics such as fingerprint, hand geometry, iris to their system that people have to touch it. Also, the face recognition system requires only a few devices such as a camera and keypad, so it is easy to apply it to the real world. The face recognition algorithms open to the public are separated by their analysis method differ from what characteristic of the human face use. There are PCA (principal Component Analysis), ICA (Independent Component Analysis), FDA (Fisher Discriminant Analysis). Among these, physiological data of encrypted form is translated utilizing PCA which is the most fundamental algorithm that analyze face feature, and we suggests design method of user authentication system that can do send-receive fast and exactly.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.33-36
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• 2001

We introduce, in this paper, the face recognition method that improves recognition rate and training time in eigen system. To increase recognition rate we use Gabor filter. To reduce the increasing training time owing to use Gabor filtering, we extract new feature vectors that are made with average and standard deviation. In experimental results, we get higher recognition rate and shorter training time in improved system than it in original eigen system.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1207-1214
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• 2019

Technologies of the 4th industrial revolution have been unknowingly seeping into our lives. Many IoT based home security systems are using the convolutional neural network(CNN) as good biometrics to recognize a face and protect home and family from intruders since CNN has demonstrated its excellent ability in image recognition. In this paper, three layouts of CNN for 2D and 2.5D image of small dataset with various input image size and filter size are explored. The simulation results show that the layout of CNN with 50*50 input size of 2.5D image, 2 convolution and max pooling layer, and 3*3 filter size for small dataset of 2.5D image is optimal for a home security system with recognition accuracy of 0.966. In addition, the longest CPU time consumption for one input image is 0.057S. The proposed layout of CNN for a face recognition is suitable to control the actuators in the home security system because a home security system requires good face recognition and short recognition time.

• Journal of Multimedia Information System
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• v.7 no.4
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• pp.239-248
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• 2020

The performance of the facial recognition system is determined by many technical factors. Further, most of the technical factors have been realized or are still in continued research. The recognition rate has a great influence on performance not only by technical factors but also by other factors. However, researchers are trying to improve the recognition rate by focusing only on technical factors. The mechanism of recognizing is to compare a face image obtained by photography to an already stored face image and determine the score of the similarity. However, if the photographed image is damaged by external light, even a system with a good algorithm will fail to recognize it. Therefore, it is important to prevent the disturbance of light entering from the outside, so it should be blocked, but the camera will not work without light. Thus, it is proposed that a method to secure the external light but block the disturbance of light that affects photography. A method of blocking disturbance light is to use a polarization filter. There are three polarization methods: circular polarization, linear polarization, and elliptical polarization. In this paper, an experiment was performed to overcome disturbance of light using only a circularly polarized filter. In addition, a lighting system that reproduces disturbance light was provided for the experiment, and light of varying intensities and angles was installed to affect the face recognition camera. As a result of actual application, it was determined that a very improved recognition performance in various disturbance light environments.