• Title/Summary/Keyword: Recognition Comparison

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Face Recognition using Karhunen-Loeve projection and Elastic Graph Matching (Karhunen-Loeve 근사 방법과 Elastic Graph Matching을 병합한 얼굴 인식)

  • 이형지;이완수;정재호
    • Proceedings of the IEEK Conference
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    • 2001.06d
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    • pp.231-234
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    • 2001
  • This paper proposes a face recognition technique that effectively combines elastic graph matching (EGM) and Fisherface algorithm. EGM as one of dynamic lint architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, the linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional method, the proposed approach could obtain satisfactory results in the perspectives of recognition rates and speeds. Especially, we could get maximum recognition rate of 99.3% by leaving-one-out method for the experiments with the Yale Face Databases.

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Enhanced Independent Component Analysis of Temporal Human Expressions Using Hidden Markov model

  • Lee, J.J.;Uddin, Zia;Kim, T.S.
    • 한국HCI학회:학술대회논문집
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    • 2008.02a
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    • pp.487-492
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    • 2008
  • Facial expression recognition is an intensive research area for designing Human Computer Interfaces. In this work, we present a new facial expression recognition system utilizing Enhanced Independent Component Analysis (EICA) for feature extraction and discrete Hidden Markov Model (HMM) for recognition. Our proposed approach for the first time deals with sequential images of emotion-specific facial data analyzed with EICA and recognized with HMM. Performance of our proposed system has been compared to the conventional approaches where Principal and Independent Component Analysis are utilized for feature extraction. Our preliminary results show that our proposed algorithm produces improved recognition rates in comparison to previous works.

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Recognition of Car License Plates Using Fuzzy Clustering Algorithm

  • Cho, Jae-Hyun;Lee, Jong-Hee
    • Journal of information and communication convergence engineering
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    • v.6 no.4
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    • pp.444-447
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    • 2008
  • In this paper, we proposed the recognition system of car license plates to mitigate traffic problems. The processing sequence of the proposed algorithm is as follows. At first, a license plate segment is extracted from an acquired car image using morphological features and color information, and noises are eliminated from the extracted license plate segment using line scan algorithm and Grassfire algorithm, and then individual codes are extracted from the license plate segment using edge tracking algorithm. Finally the extracted individual codes are recognized by an FCM algorithm. In order to evaluate performance of segment extraction and code recognition of the proposed method, we used 100 car images for experiment. In the results, we could verify the proposed method is more effective and recognition performance is improved in comparison with conventional car license plate recognition methods.

Emotion Recognition Using Tone and Tempo Based on Voice for IoT (IoT를 위한 음성신호 기반의 톤, 템포 특징벡터를 이용한 감정인식)

  • Byun, Sung-Woo;Lee, Seok-Pil
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.1
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    • pp.116-121
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    • 2016
  • In Internet of things (IoT) area, researches on recognizing human emotion are increasing recently. Generally, multi-modal features like facial images, bio-signals and voice signals are used for the emotion recognition. Among the multi-modal features, voice signals are the most convenient for acquisition. This paper proposes an emotion recognition method using tone and tempo based on voice. For this, we make voice databases from broadcasting media contents. Emotion recognition tests are carried out by extracted tone and tempo features from the voice databases. The result shows noticeable improvement of accuracy in comparison to conventional methods using only pitch.

Human Iris Recognition using Wavelet Transform and Neural Network

  • Cho, Seong-Won;Kim, Jae-Min;Won, Jung-Woo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.3 no.2
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    • pp.178-186
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    • 2003
  • Recently, many researchers have been interested in biometric systems such as fingerprint, handwriting, key-stroke patterns and human iris. From the viewpoint of reliability and robustness, iris recognition is the most attractive biometric system. Moreover, the iris recognition system is a comfortable biometric system, since the video image of an eye can be taken at a distance. In this paper, we discuss human iris recognition, which is based on accurate iris localization, robust feature extraction, and Neural Network classification. The iris region is accurately localized in the eye image using a multiresolution active snake model. For the feature representation, the localized iris image is decomposed using wavelet transform based on dyadic Haar wavelet. Experimental results show the usefulness of wavelet transform in comparison to conventional Gabor transform. In addition, we present a new method for setting initial weight vectors in competitive learning. The proposed initialization method yields better accuracy than the conventional method.

Performance Comparison between the PMC and VTS Method for the Isolated Speech Recognition in Car Noise Environments (자동차 잡음환경 고립단어 음성인식에서의 VTS와 PMC의 성능비교)

  • Chung, Yong-Joo;Lee, Seung-Wook
    • Speech Sciences
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    • v.10 no.3
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    • pp.251-261
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    • 2003
  • There has been many research efforts to overcome the problems of speech recognition in noisy conditions. Among the noise-robust speech recognition methods, model-based adaptation approaches have been shown quite effective. Particularly, the PMC (parallel model combination) method is very popular and has been shown to give considerably improved recognition results compared with the conventional methods. In this paper, we experimented with the VTS (vector Taylor series) algorithm which is also based on the model parameter transformation but has not attracted much interests of the researchers in this area. To verify the effectiveness of it, we employed the algorithm in the continuous density HMM (Hidden Markov Model). We compared the performance of the VTS algorithm with the PMC method and could see that the it gave better results than the PMC method.

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Comparison of invariant pattern recognition algorithms (불변 패턴인식 알고리즘의 비교연구)

  • 강대성
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.8
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    • pp.30-41
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    • 1996
  • This paper presents a comparative study of four pattern recognition algorithms which are invariant to translations, rotations, and scale changes of the input object; namely, object shape features (OSF), geometrica fourier mellin transform (GFMT), moment invariants (MI), and centered polar exponential transform (CPET). Pattern description is obviously one of the most important aspects of pattern recognition, which is useful to describe the object shape independently of translation, rotation, or size. We first discuss problems that arise in the conventional invariant pattern recognition algorithms, or size. We first discuss problems that arise in the coventional invariant pattern recognition algorithms, then we analyze their performance using the same criterion. Computer simulations with several distorted images show that the CPET algorithm yields better performance than the other ones.

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A Facial Expression Recognition Method Using Two-Stream Convolutional Networks in Natural Scenes

  • Zhao, Lixin
    • Journal of Information Processing Systems
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    • v.17 no.2
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    • pp.399-410
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    • 2021
  • Aiming at the problem that complex external variables in natural scenes have a greater impact on facial expression recognition results, a facial expression recognition method based on two-stream convolutional neural network is proposed. The model introduces exponentially enhanced shared input weights before each level of convolution input, and uses soft attention mechanism modules on the space-time features of the combination of static and dynamic streams. This enables the network to autonomously find areas that are more relevant to the expression category and pay more attention to these areas. Through these means, the information of irrelevant interference areas is suppressed. In order to solve the problem of poor local robustness caused by lighting and expression changes, this paper also performs lighting preprocessing with the lighting preprocessing chain algorithm to eliminate most of the lighting effects. Experimental results on AFEW6.0 and Multi-PIE datasets show that the recognition rates of this method are 95.05% and 61.40%, respectively, which are better than other comparison methods.

Low Resolution Rate Face Recognition Based on Multi-scale CNN

  • Wang, Ji-Yuan;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.21 no.12
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    • pp.1467-1472
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    • 2018
  • For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

Revenue Recognition in Terms of Accounting and Tax According to Incoterms (인코텀즈 계약조건에 따른 회계 및 세무적 수익인식시점)

  • Han, Ki-Moon;Lee, Jang-Soon
    • Korea Trade Review
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    • v.41 no.1
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    • pp.1-20
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    • 2016
  • This study is to help international traders in conducting tax and accounting works as to revenue recognition when a shipment incurs in accordance with Incoterms 2010. It is true that some are well familiar with Incoterms but have lack of accounting and tax application. In respect of tax operation, there are several regulations, such as Income Tax Law, VAT Law, which are applied differently according to sales points. Sales points and revenue recognition are a bit different when sales are made internationally. And this study compares those revenue recognitions by comparison of related Korean laws and Incoterms 2010.

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