• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 3부
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• pp.172-177
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• 2008

생체 인식은 개인의 고유한 생체 정보를 획득하여 개인 식별에 이용하는 기술로, 그중 얼굴 인식은 사용자의 편의성과 비강제성이라는 장점이 있는 응용기술로 평가 받고 있다. 본 논문에서는 얼굴인식 기술동향을 살펴보고 얼굴 영역 추출, 특정 추출, 매칭을 포함한 시스템에 대해 논한다. 얼굴 영역 추출에는 얼굴 형판 정합 방법과 얼굴 요소의 검출에 의한 방법을, 특정 추출에서는 PCA 와 LDA 등의 방법을, 그리고 매칭을 통한 인증 단계에서는 최근접 분류기를 소개한다. 다양한 얼굴 인식 기법들이 제시됨에 따라 공인된 성능 평가 방법이 필요하게 되는데, 대용량 표준 얼굴 DE의 구축과 얼굴 인식 성능 평가 방법 개발의 필요성을 제시한다. 향후 얼굴인식 시스템에서는 조명, 자세, 표정의 변화를 어떻게 보정하여 인식 할 것인가 하는 것이 연구되어야 할 핵심 분야로서 3차원 얼굴 영상 복원 기술을 통한 해결방법을 살펴본다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1244-1248
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• 2005

In this paper, we propose fuzzy c-means (FCM) to solve recognition errors in invariant range image, multi-pose face recognition. Scale, center and pose error problems were solved using geometric transformation. Range image face data was digitized into range image data by using the laser range finder that does not depend on the ambient light source. Then, the digitized range image face data is used as a model to generate multi-pose data. Each pose data size was reduced by linear reduction into the database. The reduced range image face data was transformed to the gradient face model for facial feature image extraction and also for matching using the fuzzy membership adjusted by fuzzy c-means. The proposed method was tested using facial range images from 40 people with normal facial expressions. The output of the detection and recognition system has to be accurate to about 93 percent. Simultaneously, the system must be robust enough to overcome typical image-acquisition problems such as noise, vertical rotated face and range resolution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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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 Multimedia Information System
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• 제2권1호
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• pp.171-178
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• 2015

This article describes 3 dimensional (3D) face recognition system using histogram of oriented gradients (HOG) based on face curvature. The surface curvatures in the face contain the most important personal feature information. In this paper, 3D face images are recognized by the face components: cheek, eyes, mouth, and nose. For the proposed approach, the first step uses the face curvatures which present the facial features for 3D face images, after normalization using the singular value decomposition (SVD). Fisherface method is then applied to each component curvature face. The reason for adapting the Fisherface method maintains the surface attribute for the face curvature, even though it can generate reduced image dimension. And histogram of oriented gradients (HOG) descriptor is one of the state-of-art methods which have been shown to significantly outperform the existing feature set for several objects detection and recognition. In the last step, the linear discriminant analysis is explained for each component. The experimental results showed that the proposed approach leads to higher detection accuracy rate than other methods.

• 전자공학회논문지B
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• 제30B권2호
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• pp.69-77
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• 1993

Automatic image recognition system is essential for a better man-to machine interaction. Because of the noise and deformation due to the sensor operation, it is not simple to build an image recognition system even for the fixed images. In this paper neural network which has been reported to be adequate for pattern recognition task is applied to the fixed and variational(rotation, size, position variation for the fixed image)recognition with a hope that the problems of conventional pattern recognition techniques are overcome. At fixed image recognition system. ART model is trained with face images obtained by camera. When recognizing an matching score. In the test when wigilance level 0.6 - 0.8 the system has achievel 100% correct face recognition rate. In the variational image recognition system, 65 invariant moment features sets are taken from thirteen persons. 39 data are taken to train multi-layer perceptron and other 26 data used for testing. The result shows 92.5% recognition rate.

• 한국멀티미디어학회논문지
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• 제22권8호
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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.

• 디지털산업정보학회논문지
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• 제8권2호
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• pp.31-40
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• 2012

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit-card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two-dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub-images. Then, two-dimensional principal component analysis algorithm is separately applied to each sub-images. The individual scores obtained from two sub-images are integrated using a weighted-summation rule, and the fused-score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU-PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.148-153
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권1호
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• pp.71-76
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.412-420
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• 2020

This paper proposes a performance-improving face recognition system based on a super resolution method for low-resolution images. The conventional face recognition algorithm has a rapidly decreased accuracy rate due to small image resolution by a distance. To solve the previously mentioned problem, this paper generates a super resolution images based o deep learning method. The proposed method improved feature information from low-resolution images using a super resolution method and also applied face recognition using a feature extraction and an classifier. In experiments, the proposed method improves the face recognition rate when compared to conventional methods.