• 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 Information Technology Services
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• v.6 no.3
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• pp.241-249
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• 2007

This paper describes a real-time face tracking system using effective detector and Kalman filter. In the proposed system, an image is separated into a background and an object using a real-time updated face color for effective face detection. The face features are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. The moving face is traced with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. The proposed system sets up an initial skin color and updates a region of a skin color through a moving skin color in a real time. It is possible to remove a background which has a similar color with a skin through updating a skin color in a real time. Also, as reducing a potential-face region using a skin color, the performance is increased up to 50% when comparing to the case of extracting features from a whole region.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2645-2648
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• 2003

In this paper, real-time and reconfiguable hardware filter for face recognition is proposed and implemented on FPGA chip using verilog-HDL. In general, face recognition is considerably difficult because it is influenced by noises or the variation of illumination. Some of the commonly used filters such s histogram equalization filter, contrast stretching filter for image enhancement and illumination compensation filter are proposed for realizing more effective illumination compensation. The filter proposed in this paper was designed and verified by debugging and simulating on hardware. Experimental results show that the proposed filter system can generate selective set of real-time reconfiguable hardware filters suitable for face recognition in various situation.

• Journal of International Society for Simulation Surgery
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• v.2 no.2
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• pp.64-66
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• 2015

Purpose : In this paper we proposed cascade feature filter and projection method for rapid human face recognition for the large-scale high-dimensional face database. Materials and Methods : The relevant features are selected from the large feature set using Fast Correlation-Based Filter method. After feature selection, project them into discriminant using Principal Component Analysis or Linear Discriminant Analysis. Their cascade method reduces the time-complexity without significant degradation of the performance. Results : In our experiments, the ORL database and the extended Yale face database b were used for evaluation. On the ORL database, the processing time was approximately 30-times faster than typical approach with recognition rate 94.22% and on the extended Yale face database b, the processing time was approximately 300-times faster than typical approach with recognition rate 98.74 %. Conclusion : The recognition rate and time-complexity of the proposed method is suitable for real-time face recognition system on the large-scale high-dimensional face database.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.6 no.4
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• pp.417-426
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• 1994

Filter media performance was evaluated using monodisperse NaCl particles with Differential Mobility Analyzer and Ultrafine Condensation Particle Counter. Low or medium performance filters show that the most penetrating particles size(MPPS) is around $0.3{\mu}m$ in diameter and is shifted to smaller sizes as the filter face velocity increases. However, HEPA and ULPA filters show MPPS is around $0.15{\mu}m$ in diameter and is also shifted to $0.1{\mu}m$ in diameter as the face velocity increases. In case of electret filter, the MPPS is found around $0.04{\mu}m$ region for Boltzmann charge equilibrium particles. There is a tendency of strong collection efficiency decrease for large particles as the face velocity increases on the contrary to the other filters. One of the medium performance filter efficiency was compared with filtration theory and the good agreetment was found in the experimental range.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.4
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• pp.400-403
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• 2008

In this work, an individual trespassing detector using a PIR sensor to detect infrared rays only between the range of $9.4{\sim}10.4{\mu}m$ radiated from the body is proposed. This detector using FIR sensor detects not insect or object but human body, It doesn't restrict the inhabitant's behavior because the filter of pm sensor is designed to have face angle and the detector only detects the window area. The existing wide angle filter, RIR sensor, detects $30^{\circ}$ angle while the face angle filter sensor on this paper detects $11^{\circ}$ angle with 3cm of face angle filter from 2m of detecting distance. In case of interruption of electric power, 250mAh of lithium-ion battery has worked for 10 hours consuming 22mA in normal state. Meanwhile, in case of interruption of electric power, 250mAh of battery has worked for 4 hours consuming 60mA in trespassing detecting state. Projector, receptor, controller and alarm are put on one PCB in order to make it convenient to install without any special installation skill.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.26-35
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• 2007

We present a robust face tracking system from the sequence of video images based on effective detector and Kalman filter. To construct the effective face detector, we extract the face features using the five types of simple Haar-like features. Extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. We trace the moving face with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. To make a real-time tracking system, we reduce processing time by adjusting the frequency of face detection. In this experiment, the proposed system showed an average tracking rate of 95.5% and processed at 15 frames per second. This means the system is robust enough to track faces in real-time.

• Journal of Information Display
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• v.6 no.3
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• pp.26-29
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• 2005

Nanosize cobalt aluminate($CoAl_2O_4$) power was coated as filter layer for us to improve the color purity and contrast performances on the inner face of CRT panel. We simulated color properties by measuring the transmittance and thickness of the coated filter layer. Contrast performance could be improved and color gamut was also changed by the selective light absorption of filter layer at 580${\sim}$605 nm.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1692-1699
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• 2004

Many researchers have developed various techniques for detection of human faces in ordinary still images. Face detection is the first imperative step of human face recognition systems. The two main problems of human face detection are how to cutoff the running time and how to reduce the number of false positives. In this paper, we present frontal and near-frontal face detection algorithm in still gray images using a thick edge image and neural network. We have devised a new filter that gets the thick edge image. Our overall scheme for face detection consists of two main phases. In the first phase we describe how to create the thick edge image using the filter and search for face candidates using a whole face detector. It is very helpful in removing plenty of windows with non-faces. The second phase verifies for detecting human faces using component-based eye detectors and the whole face detector. The experimental results show that our algorithm can reduce the running time and the number of false positives.

• International Journal of Contents
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• v.9 no.3
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• pp.9-14
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• 2013

In this paper, we will examine single face tracking algorithms with scaling function in a mobile device. Face detection and tracking either in PC or mobile device with scaling function is an unsolved problem. Standard single face tracking method with particle filter has a problem in tracking the objects where the object can move closer or farther from the camera. Therefore, we create an algorithm which can work in a mobile device and perform a scaling function. The key idea of our proposed method is to extract the average of skin color in face detection, then we compare the skin color distribution between the detected face and the tracking face. This method works well if the face position is located in front of the camera. However, this method will not work if the camera moves closer from the initial point of detection. Apart from our weakness of algorithm, we can improve the accuracy of tracking.