• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.511-518
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• 2009

The surveillance robot has been an important component in the field of service robot industry. In the surveillance robot technology, one of the most important technology is to identify a person. In this paper, we propose a gait recognition method based on contourlet and fuzzy LDA (Linear Discriminant Analysis) for surveillance robots. After decomposing a gait image into directional subband images by contourlet, features are obtained in each subband by the fuzzy LDA. The final gait recognition is performed by a fusion technique that effectively combines similarities calculated respectively in each local subband. To show the effectiveness of the proposed algorithm, various experiments are performed for CBNU and NLPR DB datasets. From these, we obtained better classification rates in comparison with the result produced by previous methods.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.823-831
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• 2019

In recent years, there has been developed systems such as a surveillance system and access control using a face recognition function instead of a password or an RFID chip, thereby reducing the risk of falsification. Moreover, deep learning technology has been applied to real-time face recognition technology in video, so it makes possible the development of access control system that improves the accuracy of recognition and efficiency of management. In this paper, we propose a real-time access management system based on face recognition using ResNet. The system is based on web server, which make it possible to manage the access by recognizing the person of the image through the camera and access information stored in the database. It can be accessed by a user application to receive various information. The implemented system identifies a person in real time and allows access control by accurately distinguishing whether they are members or not, and the test results can recognize in 0.2 seconds. The accuracy of recognition rate is up to about 97% depending on the experiment environment. With this system, access can be managed quickly and effectively, even many people rush to it.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2339-2345
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• 2011

Gait recognition is the method to identify the person who walks in front of camera using characteristics of individuals by a sequence of images of walking people. The accuracy of biometric such as fingerprint or iris is very high; however, to provide information needs downsides which allow users to direct contact or close-up, etc. There have been many studies in gait recognition because it could capture images and analysis characteristics far from a person. In order to recognize the gait of person needs a continuous sequence of walking which can be distinguished from the individuals should be extracted features rather than an single image. Therefore, this paper proposes a method of gait recognition that the motion of objects in sequence is described the characteristics of a shape sequence descriptor, and through a variety of experiments can show possibility as a recognition technique.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.17-23
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• 2019

In this paper, I propose a system development methodology that accepts images taken by camera attached to drone in real time while controlling mini drone and recognize and confirm the face of certain person. For the development of this system, OpenCV, Python related libraries and the drone SDK are used. To increase face recognition ratio of certain person from real-time drone images, it uses Deep Learning-based facial recognition algorithm and uses the principle of Triples in particular. To check the performance of the system, the results of 30 experiments for face recognition based on the author's face showed a recognition rate of about 95% or higher. It is believed that research results of this paper can be used to quickly find specific person through drone at tourist sites and festival venues.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.197-204
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• 2008

It is well known that the tracking a certain person is a vary needed technic in the humanoid robot. In robot technic, we should consider three aspects that is cloth color matching, face recognition and motion analysis. Because a robot technic use some sensors, it is many different with the robot technic to track a certain person through the CCTV images. A system speed should be fast in CCTV images, hence we must have small calculation numbers. We need the statistical variable for color matching and we adapt the eigen-face for face recognition to speed up the system. In this situation, motion analysis have to added for the propose of the efficient detecting system. But, in many motion analysis systems, the speed and the recognition rate is low because the system operates on the all image area. In this paper, we use the moving energy only on the face area which is searched when the face recognition is processed, since the moving energy has low calculation numbers. When the proposed algorithm has been compared with Girondel, V. et al's method for experiment, we obtained same recognition rate as Girondel, V., the speed of the proposed algorithm was the more faster. When the LDA has been used, the speed was same and the recognition rate was better than Girondel, V.'s method, consequently the proposed algorithm is more efficient for tracking a certain person.

• Journal of Information Technology Applications and Management
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• v.11 no.4
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• pp.95-102
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• 2004

This paper proposes a method of segmenting compound nouns which include unregistered nouns into a correct combination of unit nouns using characteristics of person's names, loanwords, and location names. Korean person's name is generally composed of 3 syllables, only relatively small number of syllables is used as last names, and the second and the third syllables combination is somewhat restrictive. Also many person's names appear with clue words in compound nouns. Most loanwords have one or more syllables which cannot appear in Korean words, or have sequences of syllables different from usual Korean words. Location names are generally used with clue words designating districts in compound nouns. Use of above characteristics to analyze compound nouns not only makes segmentation more accurate, helps natural language systems use semantic categories of those unregistered nouns. Experimental results show that the precision of our method is approximately 98% on average. The precision of human names and loanwords recognition is about 94% and about 92% respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1710-1717
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• 2006

Present age the allotted span is increasing as becoming agining society gradually by scientific development and handicapped person who have native, acquired drag as long as is in mechanization of life culture is increasing. In this research to control robot through speech recognition controlling robot for handicapped person.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.115.1-115
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• 2001

According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users. A computer virus is one of program on computer and has abilities of self reproduction and destruction like a virus of biology. And hacking is to rob a person´s data in a intruded computer and to delete data in a person´s computer from the outside. To block hacking that is intrusion of a person´s computer and the computer virus that destroys data, a study for intrusion-detection of system and virus detection using a biological immune system is in progress. In this paper, we make a mood of positive selection and negative selection of self-recognition process that is ability of ...

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.193-200
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• 2019

Hearing impaired people are living in a voice culture area, but due to the difficulty of communicating with normal people using sign language, many people experience discomfort in daily life and social life and various disadvantages unlike their desires. Therefore, in this paper, we study a sign language translation system for communication between a normal person and a hearing impaired person using sign language and implement a prototype system for this. Previous studies on sign language translation systems for communication between normal people and hearing impaired people using sign language are classified into two types using video image system and shape input device. However, existing sign language translation systems have some problems that they do not recognize various sign language expressions of sign language users and require special devices. In this paper, we use machine learning method of artificial neural network to recognize various sign language expressions of sign language users. By using generalized smart phone and various video equipment for sign language image recognition, we intend to improve the usability of sign language translation system.