• Title/Summary/Keyword: 가버 웨이블릿

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Iris Recognition using Gabor Wavelet and Fuzzy LDA Method (가버 웨이블릿과 퍼지 선형 판별분석 기법을 이용한 홍채 인식)

  • Go Hyoun-Joo;Kwon Mann-Jun;Chun Myung-Geun
    • Journal of KIISE:Software and Applications
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    • v.32 no.11
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    • pp.1147-1155
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    • 2005
  • This paper deals with Iris recognition as one of biometric techniques which is applied to identify a person using his/her behavior or congenital characteristics. The Iris of a human eye has a texture that is unique and time invariant for each individual. First, we obtain the feature vector from the 2D Iris pattern having a property of size invariant and using the fuzzy LDA which is further through four types of 2D Gabor wavelet. At the recognition process, we compute the similarity measure based on the correlation values. Here, since we use four different matching values obtained from four different directional Gabor wavelet and select the maximum value, it is possible to minimize the recognition error rate. To show the usefulness of the proposed algorithm, we applied it to a biometric database consisting of 300 Iris Patterns extracted from 50 subjects and finally got more higher than $90\%$ recognition rate.

Adaptive Facial Expression Recognition System based on Gabor Wavelet Neural Network (가버 웨이블릿 신경망 기반 적응 표정인식 시스템)

  • Lee, Sang-Wan;Kim, Dae-Jin;Kim, Yong-Soo;Bien, Zeungnam
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.1
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    • pp.1-7
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    • 2006
  • In this paper, adaptive Facial Emotional Recognition system based on Gabor Wavelet Neural Network, considering six feature Points in face image to extract specific features of facial expression, is proposed. Levenberg-Marquardt-based training methodology is used to formulate initial network, including feature extraction stage. Therefore, heuristics in determining feature extraction process can be excluded. Moreover, to make an adaptive network for new user, Q-learning which has enhanced reward function and unsupervised fuzzy neural network model are used. Q-learning enables the system to ge optimal Gabor filters' sets which are capable of obtaining separable features, and Fuzzy Neural Network enables it to adapt to the user's change. Therefore, proposed system has a good on-line adaptation capability, meaning that it can trace the change of user's face continuously.

Real-time Recognition System of Facial Expressions Using Principal Component of Gabor-wavelet Features (표정별 가버 웨이블릿 주성분특징을 이용한 실시간 표정 인식 시스템)

  • Yoon, Hyun-Sup;Han, Young-Joon;Hahn, Hern-Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.6
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    • pp.821-827
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    • 2009
  • Human emotion can be reflected by their facial expressions. So, it is one of good ways to understand people's emotions by recognizing their facial expressions. General recognition system of facial expressions had selected interesting points, and then only extracted features without analyzing physical meanings. They takes a long time to find interesting points, and it is hard to estimate accurate positions of these feature points. And in order to implement a recognition system of facial expressions on real-time embedded system, it is needed to simplify the algorithm and reduce the using resources. In this paper, we propose a real-time recognition algorithm of facial expressions that project the grid points on an expression space based on Gabor wavelet feature. Facial expression is simply described by feature vectors on the expression space, and is classified by an neural network with its resources dramatically reduced. The proposed system deals 5 expressions: anger, happiness, neutral, sadness, and surprise. In experiment, average execution time is 10.251 ms and recognition rate is measured as 87~93%.

Study on the Implementation of Primitive Visual Cortex Model in Retina Using Gabor Wavelet (가버 웨이블릿을 이용한 원시 시각 피질 모델 구현에 관한 연구)

  • Lee, Youngseok
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.13 no.6
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    • pp.477-482
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    • 2020
  • The human visual cortex has the characteristic that reacts sensitively to stimuli with special directional or temporal frequency changes while it is insensitive to selective stimuli of spatial phases. In this paper we implemented the model of complex cell using an image estimation iterative algorithm by Gabor wavelet transform. The performance of implemented model evaluated the consistency between the physiological experimental results in related papers. The implemented model is limited in the complete model of the receptive field in the retina where simple cells and complex cells are distributed together. But the implemented model express the reaction of the complex cells from the point of view of the detection of corners and edges.

Landmark Detection Using 3D Gobor Wavelet (3D 모델과 가버 웨이블릿을 이용한 특징점 검출)

  • Kim, Dae-Hwan;Oh, Du-Sik;Jeon, Seoung-Seon;Kim, Jae-Min;Cho, Seong-Won
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.401-402
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    • 2007
  • In this paper, we propose an automatic method to finding corresponding points. One 2D image can be changed 3D shape by 3D model. The main idea is using gabor wavelet values from 3D model. And Elastic Bunch Graph Matching algorithm is more stable in 3D model.

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Gabor and Wavelet Texture Descriptors in Representing Textures in Arbitrary Shaped Regions (임의의 영역 안에 텍스처 표현을 위한 Wavelet및 Gabor 텍스처 기술자와 성능평가)

  • Sim Dong-Gyu
    • Journal of Korea Multimedia Society
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    • v.9 no.3
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    • pp.287-295
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    • 2006
  • This paper compares two different approaches based on wavelet and Gabor decomposition towards representing the texture of an arbitrary region. The Gabor-domain mean and standard deviation combination is considered to be best in representing the texture of rectangular regions. However, texture representation of arbitrary regions would enable generalized object-based image retrieval and other applications in the future. In this study, we have found that the wavelet features perform better than the Gabor features in representing the texture of arbitrary regions. Particularly, the wavelet-domain standard deviation and entropy combination results in the best retrieval accuracy. Based on our experiment with texture image sets, we present and compare tile retrieval accuracy of multiple wavelet and Gabor feature combinations.

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