• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.400-405
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• 2005

This paper analyzes the performance of a face recognition algorithm using the eigenfaces method. In the absence of robust personal recognition schemes, a biometric recognition system has essentially researched to improve their shortcomings. A face recognition system in biometries is widely researched in the field of computer vision and pattern recognition, since it is possible to comprehend intuitively our faces. The proposed system projects facial images onto a feature space that effectively expresses the significant variations among known facial images. The significant features are known as 'eigenfaces', because they are the eigenvectors(principal components) of the set of faces. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and to recognize a particular face it is necessary only to compare these weights to those of known individuals. In order to analyze the performance of the system, we develop a face recognition system by using Harvard database in Harvard Robotics Laboratory. We present the recognition rate according to variations on the lighting condition, numbers of the employed eigenfaces, and existence of a pre-processing step. Finally, we construct a rejection curve in order to investigate the practicability of the recognition method using the eigenfaces.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.892-898
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• 2012

Rough classification methods are used to improving the recognition speed in many character recognition problems. In this case, some irreversible result can occur by an error in rough classification. Methods for duplicating each model in several classes are used in order to reduce this risk. But the errors by rough classfication can not be completely ruled out by these methods. In this paper, an recognition method is proposed to increase speed that matches models selectively without any increase in error. This method constructs a model graph using similarity between models. Then a search process begins from a particular point in the model graph. In this process, matching of unnecessary models are reduced that are not similar to the input pattern. In this paper, the proposed method is applied to the recognition problem of handwriting numbers and upper/lower cases of English alphabets. In the experiments, the proposed method was compared with the basic method that matches all models with input pattern. As a result, the same recognition rate, which has shown as the basic method, was obtained by controlling the out-degree of the model graph and the number of maintaining candidates during the search process thereby being increased the recognition speed to 2.45 times.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.503-523
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• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1770-1776
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• 2021

For hand expression recognition, hand pose recognition based on the static shape of the hand and hand gesture recognition based on hand movement are used together. In this paper, we proposed a hand expression recognition method that recognizes symbols based on the trajectory of a hand movement on a virtual blackboard. In order to recognize a sign drawn by hand on a virtual blackboard, not only a method of recognizing a sign from a hand movement, but also hand pose recognition for finding the start and end of data input is also required. In this paper, MediaPipe was used to recognize hand pose, and LSTM(Long Short Term Memory), a type of recurrent neural network, was used to recognize hand gesture from time series data. To verify the effectiveness of the proposed method, it was applied to the recognition of numbers written on a virtual blackboard, and a recognition rate of about 94% was obtained.

• Convergence Security Journal
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• v.22 no.5
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• pp.3-9
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• 2022

Recently, in order to build a cyber physical system(CPS) that is a technology related to the 4th industry, the construction of the virtual control system for physical model and control circuit simulation is increasingly required in various industries. It takes a lot of time and money to convert documents that are not electronically documented through direct input. For this, it is very important to digitize a large number of drawings that have already been printed through object recognition using artificial intelligence. In this paper, in order to accurately recognize objects in drawings and to utilize them in various applications, a recognition technique using artificial intelligence by analyzing the characteristics of objects in drawing was proposed. In order to improve the performance of object recognition, each object was recognized and then an intermediate file storing the information was created. And the recognition rate of the next recognition target was improved by deleting the recognition result from the drawing. In addition, the recognition result was stored as a standardized format document so that it could be utilized in various fields of the control system. The excellent performance of the technique proposed in this paper was confirmed through the experiments.

• Journal of Korean Public Health Nursing
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• v.21 no.2
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• pp.217-229
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• 2007

Purpose: This study sought to investigate the relationship between smoking, drinking and the mental health of adolescents. Method: The study subjects included 1,092 randomly sampled third-year middle and high school students from N-City and the L-Kun area in Chonbuk Province. The data were collected using an anonymous questionnaire developed by the Ministry of Health and Welfare(2005). The data were analyzed using the SPSSWIN 15.0 Program. Result: The smoking rate in the adolescents was 10.1%, and the drinking rate was 43.2%. The stress recognition rate in normal daily life was 45.5%, the frequency of depressive symptoms was 33.2% and 40.0% of the subjects reported that they wanted to die at some point within the past year. The rate of stress recognition, depressive symptom experience, suicidal planning and attempt in smokers and drinkers was higher than that in non-smokers and non-drinkers (p<.05). Especially, the frequency of suicidal thoughts was higher in the drinkers than in the non-drinkers (p<.05). Conclusion: The smoking and drinking status of the adolescents was correlated with their mental health a sit relates to stress, depression and suicide. Therefore, an effective program for the prohibition of cigarette smoking and alcohol consumption is needed in order to prevent smoking and alcohol consumption in adolescents and improve their mental health status.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.408-418
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• 2015

This paper describes the design and implementation of situation recognition system with smart phone sensors, which recognizes the dangerous situation at anytime, anywhere through intuitive data analysis of the combination of the sensor. The implemented system consists of wearable heart rate sensor and acceleration sensor of smart phone instead of existing sensor that is attached to the body. It is also designed to get more effective results of recognition about the dangerous situation using merged displacement values of acceleration sensor and heart rate sensor which are measured in the process of recognizing dangerous situations. This research, in accordance with the wide penetration of smartphones, achieves the fast status determination through the combination of an acceleration sensor and a heart rate sensor applied to its own status perception algorithm for anyone who needs the stable perception of risk without the need for a separate provision of the sensor.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1930-1932
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• 2004

SOM(Self Organizing Map) algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. For the purpose, partial discharge data were acquired and analysed from the artificial defects in GIS. As a result, basically the pattern recognition rate of BP algorithm was found out to be better than that of SOM algorithm. However, SOM algorithm showed a great on-site-applicability such as ability of suggesting new-pattern-possibility. Therefore, through increasing pattern recognition rate it is possible to apply SOM algorithm to partial discharge analysis. Also, for the image processing method it is required the normalization of the PRPDA graph. However, due to the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.11-16
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• 2005

Recently, there are a lot of endeavor to implement a fuzzy theory to artificial neural network. Goh proposed the fuzzy single layer perceptron algorithm and advanced fuzzy perceptron based on the generalized delta rule to solve the XOR Problem and the classical Problem. However, it causes an increased amount of computation and some difficulties in application of the complicated image recognition. In this paper, we propose an enhanced fuzzy single layer Perceptron using the dynamic adjustment of threshold. This method is applied to the XOR problem, which used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for image application. In a result of experiment, it does not always guarantee the convergence. However, the network show improved the learning time and has the high convergence rate.

• Ocean Systems Engineering
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• v.4 no.1
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• pp.53-62
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• 2014

The detection of ship wakes image can demonstrate substantial information regarding on a ship, such as its tonnage, type, direction, and speed of movement. Consequently, the wake target recognition is a favorable way for ship identification. This paper proposes a Local Binary Pattern (LBP) approach to extract image features (wakes) for training an Improved Back Propagation Artificial Neural Network (IBPANN) to identify ship speed. This method is applied to sort and recognize the ship wakes of five different speeds images, the result shows that the detection accuracy is satisfied as expected, the average correctness rates of wakes target recognition at the five speeds may be achieved over 80%. Specifically, the lower ship's speed, the better accurate rate, sometimes it's accuracy could be close to 100%. In addition, one significant feature of this method is that it can receive a higher recognition rate than the nearest neighbor classification method.