• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1247-1256
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• 2012

There are many filter based image processing algorithms and they usually require a huge amount of computations and memory accesses making it hard to attain a real-time performance, expecially in embedded applications. In this paper, we propose a pipelined hardware structure of the filter based face detection algorithm to show that the real time performance can be achieved by hardware design. In our design, the whole computation is divided into three pipeline stages: resizing the image (Resize), Transforming the image (ICT), and finding candidate area (Find Candidate). Each stage is optimized by considering the parallelism of the computation to reduce the number of cycles and utilizing the line memory to minimize the memory accesses. The resulting hardware uses 507 KB internal SRAM and occupies 9,039 LUTs when synthesized and configured on Xilinx Virtex5LX330 FPGA. It can operate at maximum 165MHz clock, giving the performance of 108 frame/sec, while detecting up to 20 faces.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10C
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• pp.621-628
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• 2011

The skin color has been used as important cues in the systems for detecting or recognizmg the face. However, the color difference in images under different illuminants makes it difficult to find out the skin in these systems. For solving the problem, this paper proposes a method of recovering skin colors based on well-known color constancy approaches, such as Retinex, Gray World, White Patch, and Simplified Horn. To acquire experimental images under the colored scene illumination, the effects of colored illuminants were added to source images. Next, result images, having the corrected skin color by the constancy methods, were derived from the source images. The experiment results showed that most of the skin colors in our experiments were recovered into some steady range in the color space, and that Gray World had higher performance than the other methods compared.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.29-36
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• 2008

In this paper we propose a real-time method to estimate a pointing region from two camera images. In general, a pointing target exists in the face direction when a human points to something. Therefore, we regard the direction of pointing as the straight line that connects the face position with the fingertip position. First, the method extracts two points in the face and the fingertips region by using detecting the skin color of human being. And we used the 3D geometric information to obtain a pointing detection and its region. In order to evaluate the performance, we have build up an ICIGS(Interactive Cinema Information Guiding System) with two camera and a beam project.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.9
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• pp.40-47
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• 2007

For image processing hardware, including a face detecting engine, efficient constitution of external and internal memories is a consequential point because huge memory is required to store various signal processing filters and incoming images. In this paper, we modified a face detect algerian of a general filter method for efficient hardware design. In the hardware, several memory design techniques are presented for efficient handling of image data : re-accessing avoidance with minimized internal memory usage, residing frequently accessed memory and sequence memory accessing. The hardware which can process 25 frame image data per one second with 40KB internal memory was verified by using ARM(S3C2440A) and Virtex4 FPGA and it is being fabricated as a ASIC chip using Samsung CMOS 0.18um technology.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.30-39
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• 2012

In this paper, a safe driving assistance system is proposed by detecting the status of driver's doze based on face and eye detection. By the level of the fatigue, safe driving system alarms or set the seatbelt on vibration. To reduce the effect of backward light and too strong solar light which cause a decrease of face and eye detection rate and false fatigue detection, post processing techniques like image equalization are used. Haar transform and PCA are used for face detection. By using the statistic of the face and eye structural ratio of normal Koreans, we can reduce the eye candidate area in the face, which results in reduction of the computational load. We also propose a new eye status detection algorithm based on Hough transform and eye width-height ratio, which are used to detect eye's blinking status which decides doze level by measuring the blinking period. The system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. In this paper, four algorithms are implemented and proposed algorithm is made based on the probability model and we achieves 84.88% of correct detection rate through indoor and in-car environment experiments. And also we achieves 69.81% of detection rate which is better result than that of other algorithms using IR camera.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.494-503
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• 2011

In this paper, we propose a recognition application of facial expression for laughter theraphy on smartphone. It detects face region by using AdaBoost face detection algorithm from the front camera image of a smartphone. After detecting the face image, it detects the lip region from the detected face image. From the next frame, it doesn't detect the face image but tracks the lip region which were detected in the previous frame by using the three step block matching algorithm. The size of the detected lip image varies according to the distance between camera and user. So, it scales the detected lip image with a fixed size. After that, it minimizes the effect of illumination variation by applying the bilateral symmetry and histogram matching illumination normalization. After that, it computes lip eigen vector by using PCA(Principal Component Analysis) and recognizes laughter expression by using a multilayer perceptron artificial network. The experiment results show that the proposed method could deal with 16.7 frame/s and the proposed illumination normalization method could reduce the variations of illumination better than the existing methods for better recognition performance.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.755-767
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• 2003

This paper proposes a robust fault-tolerant control framework for robot manipulators to maintain the required performance and achieve task completion in the presence of both partial joint failures and complete joint failures and uncertainties. In the case of a complete joint failure or free-swinging joint failure causing the complete loss of torque on a joint, a fully-actuated robot manipulator can be viewed as an underactuated robot manipulator. To detect and identify a complete actuator failure, an on-line fault detection operation is also presented. The proposed fault-tolerant control system contains a robust adaptive controller overcoming partial joint failures based on robust adaptive control methodology, an on-line fault detector detecting and identifying complete joint failures, and a robust adaptive controller overcoming partial and complete joint failures, and so eventually it can face and overcome joint failures and uncertainties. Numerical simulations are conducted to validate the proposed robust fault-tolerant control scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.699-704
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• 2016

In this paper, we propose a development method for a drowsiness detection system using retinex theory and edge information for vehicle safety. Detection of a drowsy state of a driver is very important because the drowsiness of driver is often the main cause of many car accidents. After acquiring an image of the entire face, we executed the pre-process step using the retinex theory. We then applied a technique for the detection of the white pixels using edge information. Experimental results showed that the proposed method improved the accuracy of detecting drowsiness to nearly 98%, and can be used to prevent a car accident caused by the driver's drowsiness.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1939-1951
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• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1821-1823
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• 2006

This paper presents a development of interface device for electro-oculogram(EOG) signal and it's application to the wireless mouse of wearable PC. The interface device is composed of five bio-electrodes for detecting oculomotor motion, several band-pass filters, instrumentation amplifier and a microprocessor. we have first analyzed impedance characteristics between skin and a bio-electrode. since the impedance highly depends on human face, it's magnitude differs from person. this interface device was applied to develop a wireless mouse for wearable PC, as a Bio Machine Interface(BMI). Where in the prompt on PC monitor is controlled by only EOG signals. this system was implemented in a Head Mount Display(HMD) unit. experimental results show the accuracy of above 90%.