• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.49.4-49
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• 2001

Digit recognition based on backpropagation neural networks, as an important application of pattern recognition, was attracted much attention. Although it has the advantages of parallel calculation, high error-tolerance, and learning capability, better recognition effects can only be achieved with some specific fixed format input of the digit image. Therefore, digit image preprocessing ability directly affects the accuracy of recognition. Here using Matlab software, the digit image was enhanced by resizing and neutral-rotating the extracted digit image, which improved the digit recognition capability of the backpropagation neural network under practical conditions. This method may also be helpful for recognition of other patterns with backpropagation neural networks.

• 한국컴퓨터정보학회논문지
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• 제10권4호
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• pp.17-27
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• 2005

본 논문에서는 문자인식과 복원을 위한 신경회로망 시스템을 제안한다. 제안하는 시스템은 인식부와 연상부로 구성되었다. 인식부에서는 ART 신경회로망의 인식성능을 개선하기 위해 불필요한 하향틀의 생성과 변화를 제한하여 효과적인 패턴인식이 가능한 모델을 제안하였다. 또한, 한글의 구조적인 특징을 능동적으로 적용할 수 있게 구성된 위치특징 추출 알고리즘을 적용하였다. 연상부에서는 Hopfield 신경회로망으로, 입력된 이미지 패턴의 복원이 가능한 모델을 구성하였다. 제안하는 시스템은 그 성능을 확인하기 위해 각 부분별 실험을 하였다. 그 결과 인식율이 개선되고 복원이 가능함을 보였다.

• 한국인공지능학회지
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• 제5권2호
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• pp.43-53
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• 2017

In this paper, the pattern of extracting the same expression is proposed by using the Android intelligent device to identify the facial expression. The understanding and expression of expression are very important to human computer interaction, and the technology to identify human expressions is very popular. Instead of searching for the emoticons that users often use, you can identify facial expressions with acamera, which is a useful technique that can be used now. This thesis puts forward the technology of the third data is available on the website of the set, use the content to improve the infrastructure of the facial expression recognition accuracy, in order to improve the synthesis of neural network algorithm, making the facial expression recognition model, the user's facial expressions and similar e xpressions, reached 66%.It doesn't need to search for emoticons. If you use the camera to recognize the expression, itwill appear emoticons immediately. So this service is the emoticons used when people send messages to others, and it can feel a lot of convenience. In countless emoticons, there is no need to find emoticons, which is an increasing trend in deep learning. So we need to use more suitable algorithm for expression recognition, and then improve accuracy.

• International Journal of Advanced Culture Technology
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• 제6권1호
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• pp.60-67
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• 2018

In this paper, the pattern of extracting the same expression is proposed by using the Android intelligent device to identify the facial expression. The understanding and expression of expression are very important to human computer interaction, and the technology to identify human expressions is very popular. Instead of searching for the symbols that users often use, you can identify facial expressions with a camera, which is a useful technique that can be used now. This thesis puts forward the technology of the third data is available on the website of the set, use the content to improve the infrastructure of the facial expression recognition accuracy, to improve the synthesis of neural network algorithm, making the facial expression recognition model, the user's facial expressions and similar expressions, reached 66%. It doesn't need to search for symbols. If you use the camera to recognize the expression, it will appear symbols immediately. So, this service is the symbols used when people send messages to others, and it can feel a lot of convenience. In countless symbols, there is no need to find symbols, which is an increasing trend in deep learning. So, we need to use more suitable algorithm for expression recognition, and then improve accuracy.

• 반도체디스플레이기술학회지
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• 제21권3호
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• pp.50-56
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• 2022

In order to process a vast amount of data, there is demand for a new system with higher processing speed and lower energy consumption. To prevent 'memory wall' in von Neumann architecture, RRAM, which is a neuromorphic device, has been researched. In this paper, we summarize the features of RRAM and propose the device structure for characteristic improvement. RRAM operates as a synapse device using a change of resistance. In general, the resistance characteristics of RRAM are nonlinear and random. As synapse device, linearity and uniformity improvement of RRAM is important to improve learning recognition rate because high linearity and uniformity characteristics can achieve high recognition rate. There are many method, such as TEL, barrier layer, NC, high oxidation properties, to improve linearity and uniformity. We proposed a new device structure of TiN/Al doped TaO_x/AlO_x/Pt that will achieve high recognition rate. Also, with simulation, we prove that the improved properties show a high learning recognition rate.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.173-180
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• 2018

This paper proposes a method to improve the performance of face recognition via super-resolution method using sparse representation and deep learning from low-resolution facial images. Recently, there have been many researches on ultra-high-resolution images using deep learning techniques, but studies are still under way in real-time face recognition. In this paper, we combine the sparse representation and deep learning to generate super-resolution images to improve the performance of face recognition. We have also improved the processing speed by designing in parallel structure when applying sparse representation. Finally, experimental results show that the proposed method is superior to conventional methods on various images.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.953-960
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• 2018

In recently, eye blink recognition, and face recognition are very popular and promising techniques. In some cases, people can use the photos and face masks to hack mobile security systems, so we propose an eye blinking detection, which finds eyes through the proportion of human face. The proposed method detects the movements of eyeball and the number of eye blinking to improve face recognition for screen unlock on the mobile devices. Experimental results show that our method is efficient and robust for the screen unlock on the mobile devices.

• 전기학회논문지
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• 제64권3호
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• pp.465-472
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• 2015

This paper presents a visual touch recognition for NUI(Natural User Interface) using Voronoi-tessellation algorithm. The proposed algorithms are three parts as follows: hand region extraction, hand feature point extraction, visual-touch recognition. To improve the robustness of hand region extraction, we propose RGB/HSI color model, Canny edge detection algorithm, and use of spatial frequency information. In addition, to improve the accuracy of the recognition of hand feature point extraction, we propose the use of Douglas Peucker algorithm, Also, to recognize the visual touch, we propose the use of the Voronoi-tessellation algorithm. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• 한국정보통신학회논문지
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• 제12권8호
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• pp.1456-1462
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• 2008

단일 생체인식 시스템의 인식률을 높이는 것은 생체인식 방법마다 취약점이 있기 때문에 그 한계가 있기 마련이다. 얼굴 인식의 경우 조명과 같은 환경적 요인으로 인식률이 저하될 수 있으며, 화자 확인의 경우도 잡음과 같은 환경적 요인으로 인식률이 크게 저하될 수 있다. 따라서 두 가지 이상의 생체특징을 결합하여 다중 생체인식 시스템을 구현함으로써 그 취약점을 보완하는 추세에 있다. 본 논문에서는 얼굴 인식과 화자 확인 시스템을 결합하여 다중 생체인식 시스템을 구현하였고, 일반적인 가중치합 알고리즘에 환경 변수를 적용하여 기존의 다중 생체 인식 시스템보다 인식률을 향상시켰다. 본 시스템은 비밀키 기반의 애플릿으로 구현되어 있으므로 웹 상의 사용자 인증을 필요로 하는 응용에 활용될 수 있다.