• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.570-579
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• 2005

Facial expression recognition technology that has potentialities for applying various fields is appling on the man-machine interface development, human identification test, and restoration of facial expression by virtual model etc. Using sequential facial images, this study proposes a simpler method for detecting human facial expressions such as happiness, anger, surprise, and sadness. Moreover the proposed method can detect the facial expressions in the conditions of the sequential facial images which is not rigid motion. We identify the determinant face and elements of facial expressions and then estimates the feature regions of the elements by using information about color, size, and position. In the next step, the direction patterns of feature regions of each element are determined by using optical flows estimated gradient methods. Using the direction model proposed by this study, we match each direction patterns. The method identifies a facial expression based on the least minimum score of combination values between direction model and pattern matching for presenting each facial expression. In the experiments, this study verifies the validity of the Proposed methods.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1397-1403
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• 2018

Today, modern people perceive skin care as part of their physical health care, and acne is a common skin disease problem that is found on the face. In this paper, an effective acne detection algorithm using CIE $L^*a^*b^*$ color space has been proposed. It is red when the pixel value of the component image $a^*$ is a positive number, so it is suitable for detecting acne in skin image. First, the skin image based on the RGB color space is subjected to light compensation through color balancing, and converted into a CIE $L^*a^*b^*$ color space. The extracted component image $a^*$ was normalized, and then the skin and acne area were estimated with the threshold values. Experimental results show that the proposed method detects acne more effectively than the conventional method based on brightness information, and the proposed method is robust against the reflected light source.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.284-291
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• 2005

I propose a framework for detecting, recognizing and classifying facial features based on learned expression patterns. The framework recognizes facial expressions by using PCA and expression HMM(EHMM) which is Hidden Markov Model (HMM) approach to represent the spatial information and the temporal dynamics of the time varying visual expression patterns. Because the low level spatial feature extraction is fused with the temporal analysis, a unified spatio-temporal approach of HMM to common detection, tracking and classification problems is effective. The proposed recognition framework is accomplished by applying posterior probability between current visual observations and previous visual evidences. Consequently, the framework shows accurate and robust results of recognition on as well simple expressions as basic 6 facial feature patterns. The method allows us to perform a set of important tasks such as facial-expression recognition, HCI and key-frame extraction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.3
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• pp.482-488
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• 2009

Mobile Ad-hoc Networks provide communication without a centralized infrastructure, which makes them suitable for communication in disaster areas or when quick deployment is needed. On the other hand, they are susceptible to malicious exploitation and have to face different challenges at different layers due to its open Ad-hoc network structure which lacks previous security measures. Denial of service (DoS) attack is one that interferes with the radio transmission channel causing a jamming attack. In this kind of attack, an attacker emits a signal that interrupts the energy of the packets causing many errors in the packet currently being transmitted. In harsh environments where there is constant traffic, a jamming attack causes serious problems; therefore measures to prevent these types of attacks are required. The objective of this paper is to carry out the simulation of the jamming attack on the nodes and determine the DoS attacks in OPNET so as to obtain better results. We have used effective anomaly detection system to detect the malicious behaviour of the jammer node and analyzed the results that deny channel access by jamming in the mobile Ad-hoc networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1120-1131
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• 2016

In this paper, we propose a smoker recognition algorithm, detecting smokers in a video sequence in order to prevent fire accidents. We use description-based method in hierarchical approaches to recognize smoker's activity, the algorithm consists of background subtraction, object detection, event search, event judgement. Background subtraction generates slow-motion and fast-motion foreground image from input image using Gaussian mixture model with two different learning-rate. Then, it extracts object locations in the slow-motion image using chain-rule based contour detection. For each object, face is detected by using Haar-like feature and smoke is detected by reflecting frequency and direction of smoke in fast-motion foreground. Hand movements are detected by motion estimation. The algorithm examines the features in a certain interval and infers that whether the object is a smoker. It robustly can detect a smoker among different objects while achieving real-time performance.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.492-503
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• 2008

With the recent development and increasing importance of personal identification systems, biometric technologies with less risk of loss or unauthorized use are being popularized rapidly. In particular, because of their high identification rate and convenience, fingerprint identification systems are being used much more commonly than other biometric systems such as iris recognition, face recognition and vein pattern recognition. However, a fingerprint identification system has the problem that artificially forged finger-prints can be used as input data. Thus, in order to solve this problem, the present study proposed a method for detecting forged fingerprints by measuring the degree of attenuation when the light from an optical fingerprint sensor passes through the finger and analyzing changes in the transmission of light over stages at fixed intervals. In order to prove improvement in the performance of the proposed system, we conducted an experiment that compared the system with an existing multi-sensor recognition system that measures also the temperature of fingerprint. According to the results of the experiment, the proposed system improved the forged fingerprint detection rate by around 32.6% and this suggests the possibility of solving the security problem in fingerprint identification systems.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.73-82
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• 2022

Marine fisheries resources face major anthropogenic threat from unregulated fishing activities; thus require precise detection for protection through marine surveillance. Korea developed an efficient land-based small fishing vessel monitoring system using real-time V-Pass data. However, those data directly do not provide information on fishing activities, thus further efforts are necessary to differentiate their activity status. In Korea, especially in Busan, longlining is practiced by many small fishing vessels to catch several types of fishes that need to be identified for proper monitoring. Therefore, in this study we have improved the existing fishing status classification method by applying Hidden Markov Model (HMM) on V-Pass data in order to further classify their fishing status into three groups, viz. non-fishing, longlining and other types of fishing. Data from 206 fishing vessels at Busan on 05 February, 2021 were used for this purpose. Two tiered HMM was applied that first differentiates non-fishing status from the fishing status, and finally classifies that fishing status into longlining and other types of fishing. Data from 193 and 13 ships were used as training and test datasets, respectively. Using this model 90.45% accuracy in classifying into fishing and non-fishing status and 88.23% overall accuracy in classifying all into three types of fishing statuses were achieved. Thus, this method is recommended for monitoring the activities of small fishing vessels equipped with V-Pass, especially for detecting longlining.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.4 no.1
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• pp.9-15
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• 2006

For the accurate safety assessment of potential radioactive waste disposal site which is located in the crystalline rock it is important to simulate the mass transportation through engineered and natural barrier system precisely, characterized by porous and fractured media respectively. In this work the methods to construct discrete fracture network for the analysis of flow and mass transport through fractured-porous medium are described. The probability density function is adopted in generating fracture properties for the realistic representation of real fractured rock. In order to investigate the intersection between a porous and a fractured medium described by a 2 dimensional rectangular and a cuboid grid respectively, an additional imaginary fracture is adopted at the face of a porous medium intersected by a fracture. In order to construct large scale flow paths an effective method to find interconnected fractures and algorithms of swift detecting connectivities between fractures or porous medium and fractures are proposed. These methods are expected to contribute to the development of numerical program for the simulation of radioactive nuclide transport through fractured-porous medium from radioactive waste disposal site.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.149-155
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• 2020

The Korea Highway Traffic Authority provides statistics that analyze the causes of traffic accidents that occurred since 2015 using the Traffic Accident Analysis System (TAAS). it was reported Through TAAS that the driver's forward carelessness was the main cause of traffic accidents in 2018. As statistics on the cause of traffic accidents, 51.2 percent used mobile phones and watched DMB while driving, 14 percent did not secure safe distance, and 3.6 percent violated their duty to protect pedestrians, representing a total of 68.8 percent. In this paper, we propose a system that has improved the advanced driver assistance system ADAS (Advanced Driver Assistance Systems) by utilizing CNN (Convolutional Neural Network) among the algorithms of Deep Learning. The proposed system learns a model that classifies the movement of the driver's face and eyes using Conv2D techniques which are mainly used for Image processing, while recognizing and detecting objects around the vehicle with cameras attached to the front of the vehicle to recognize the driving environment. Then, using the learned visual steering model and driving environment data, the hazard is classified and detected in three stages, depending on the driver's view and driving environment to assist the driver with the forward and blind spots.