• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (B)
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• pp.835-837
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• 2005

본 논문에서는 가보 웨이블릿을 통해 얼굴 이미지로부터 특징을 추출하고, 그에 Genetic Algorithm 을 통해 구한 특징점별 가중치를 적용하여 얼굴 인식을 하는 방법을 소개한다. 각 특징점별로 가중치를 적용하는 방법은, 기존의 Gabor wavelet 을 사용한 얼굴 인식 방법들에 비해 높은 인식률을 보인다. 특징점별 가중치들은 진화 알고리즘을 통해 학습 되어진다.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 10월 학술대회지
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• pp.145-148
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• 2003

To improve the recognition performance of Korean connected digit telephone speech, in this paper, both Aurora feature extraction method that employs noise reduction 2-state Wiener filter and DWFBA method are investigated and used. CMN and MRTCN are applied to static features for channel compensation. Telephone digit speech database released by SITEC is used for recognition experiments with HTK system. Experimental results has shown that Aurora feature is slightly better than MFCC and DWFBA without channel compensation. And when channel compensation is included, Aurora feature is slightly better than DWFBA with MRTCN.

• Journal of information and communication convergence engineering
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• 제13권3호
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• pp.145-151
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• 2015

We describe in this paper a hardware-based improvement scheme of a real-time automatic speech recognition (ASR) system with respect to speed by designing a parallel feature extraction algorithm on a Field-Programmable Gate Array (FPGA). A computationally intensive block in the algorithm is identified implemented in hardware logic on the FPGA. One such block is mel-frequency cepstrum coefficient (MFCC) algorithm used for feature extraction process. We demonstrate that the FPGA platform may perform efficient feature extraction computation in the speech recognition system as compared to the generalpurpose CPU including the ARM processor. The Xilinx Zynq-7000 System on Chip (SoC) platform is used for the MFCC implementation. From this implementation described in this paper, we confirmed that the FPGA platform is approximately 500× faster than a sequential CPU implementation and 60× faster than a sequential ARM implementation. We thus verified that a parallelized and optimized MFCC architecture on the FPGA platform may significantly improve the execution time of an ASR system, compared to the CPU and ARM platforms.

• 대한음성학회지:말소리
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• 제63호
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• pp.113-124
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• 2007

In this paper, we propose a use of confidence vector as an intermediate input feature for multi-stage based speech recognition architecture to improve recognition accuracy. A multi-stage speech recognition structure is introduced as a method to reduce the computational complexity of the decoding procedure and then accomplish faster speech recognition. Conventional multi-stage speech recognition is usually composed of three stages, acoustic search, lexical search, and acoustic re-scoring. In this paper, we focus on improving the accuracy of the lexical decoding by introducing a confidence vector as an input feature instead of phoneme which was used typically. We take experimental results on 220K Korean Point-of-Interest (POI) domain and the experimental results show that the proposed method contributes on improving accuracy.

• 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.

• 한국정밀공학회지
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• 제17권6호
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• pp.176-184
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• 2000

Reverse engineering (RE) is a process to create computer aided design (CAD) models from the scanned data of an existing part acquired using 3D position scanners. This paper proposes a novel methodology of extracting geometric features directly from a set of 3D scanned points, which utilizes the concepts of feature-based technology and artificial neural networks (ANNs). The use of ANN has enabled the development of a flexible feature-based RE application that can be trained to deal with various features. The following four main tasks were mainly investigated and implemented: (1) Data reduction; (2) edge detection; (3) ANN-based feature recognition; (4) feature extraction. This approach was validated with a variety of real industrial components. The test results show that the developed feature-based RE application proved to be suitable for reconstructing prismatic features such as block, pocket, step, slot, hole, and boss, which are very common and crucial in mechanical engineering products.

• 정보처리학회논문지B
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• 제9B권4호
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• pp.491-500
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• 2002

본 논문에서는 칼라 얼굴 영상으로부터 피부색 정보, 얼굴의 기하학적 특징벡터 및 안면각 정보를 이용한 실시간 얼굴검출 및 인식 알고리즘을 제안하였다. 제안한 알고리즘에서는 HSI 칼라좌표계상의 얼굴 피부색 정보와 얼굴 에지 정보를 함께 이용함으로써 얼굴 영역 검출 효율을 개선하였다. 또한 추출된 얼굴 영역으로부터 얼굴인식율 개선을 위해 얼굴 특징자들을 추출하고 추출된 얼굴 특징자들의 기하학적 관계로 구성된 얼굴 특징벡터와 얼굴 안면각 정보를 사용하여 얼굴 인식율을 개선하였다. 실험에서는 제안한 방법이 기존의 방법에 비해 얼굴 영역 검출율 뿐만 아니라 얼굴 인식율도 개선되었음을 알 수 있다.

• Journal of information and communication convergence engineering
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• 제16권3호
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• pp.160-165
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• 2018

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

• ETRI Journal
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• 제45권6호
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• pp.1022-1034
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• 2023

In this study, the impact of varying lighting conditions on recognition and decision-making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor-based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL-based images, significant feature points were selected using the scale-invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.

• 디지털산업정보학회논문지
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• 제11권3호
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• pp.99-112
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• 2015

Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.