• Title/Summary/Keyword: recognition-rate

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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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    • 대한전자공학회 2001년도 하계종합학술대회 논문집(4)
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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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Recognition of Printed and Handwritten Numerals Using Multiple Features and Modularized Neural Networks (다중 특징과 모듈화된 신경회로망을 이용한 인쇄 및 필기체 혼용 숫자 인식)

  • 류강수;김우태;진성일
    • Journal of the Korean Institute of Telematics and Electronics B
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    • 제32B권10호
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    • pp.1347-1357
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    • 1995
  • In this paper, we describe a modularized neuroclassifier for enhancing the recognition accuracy of mixed printed and handwritten numerals. This classifier combines four modularized subclassifiers using multi-layer perceptron module. The input of each subclassifier is comprised of a group of specialized feature sets. On applying this method to combining several subclassifiers for unconstrained handwritten numerals, the experimental result shows that the performance of individual subclassifier can be improved. In winner-take-all voting method, the result of subclassifier having the highest RF value is selected as the output. The generality of this classifier is tested with 1,080 printed and 3,000 handwritten numerals that was not shown in training the neural networks. Experimental results show 98.2% recognition rate. The typical recognition test with a threshold value(RF=1.5) has shown 97% recognition, 1% substitution and 2% rejection rates.

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An Experiment of a Spoken Digits-Recognition System (숫자음성 자동 인식에 관한 일실험)

  • ;安居院猛
    • Journal of the Korean Institute of Telematics and Electronics
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    • 제15권6호
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    • pp.23-28
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    • 1978
  • This paper describes a speech recognition system for ten isolated spoken digits. In this system, acoustic parameters such as zero crossing rate, log energy and three formant frequencies estimated by linear prediction method were extracted for classification and/or recognition purpose(s). The former two parameters were used for the classification of unvoiced consonants and the latter one for the recognition of vowels and voiced consonants. Promising recognition results were obtained in this experiment for ten digit utterances spoken by a male speaker.

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The Effect of the Number of Clusters on Speech Recognition with Clustering by ART2/LBG

  • Lee, Chang-Young
    • Phonetics and Speech Sciences
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    • 제1권2호
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    • pp.3-8
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    • 2009
  • In an effort to improve speech recognition, we investigated the effect of the number of clusters. In usual LBG clustering, the number of codebook clusters is doubled on each bifurcation and hence cannot be chosen arbitrarily in a natural way. To have the number of clusters at our control, we combined adaptive resonance theory (ART2) with LBG and perform the clustering in two stages. The codebook thus formed was used in subsequent processing of fuzzy vector quantization (FVQ) and HMM for speech recognition tests. Compared to conventional LBG, our method was shown to reduce the best recognition error rate by 0${\sim$}0.9% depending on the vocabulary size. The result also showed that between 400 and 800 would be the optimal number of clusters in the limit of small and large vocabulary speech recognitions of isolated words, respectively.

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Person Recognition using Ocular Image based on BRISK (BRISK 기반의 눈 영상을 이용한 사람 인식)

  • Kim, Min-Ki
    • Journal of Korea Multimedia Society
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    • 제19권5호
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    • pp.881-889
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    • 2016
  • Ocular region recently emerged as a new biometric trait for overcoming the limitations of iris recognition performance at the situation that cannot expect high user cooperation, because the acquisition of an ocular image does not require high user cooperation and close capture unlike an iris image. This study proposes a new method for ocular image recognition based on BRISK (binary robust invariant scalable keypoints). It uses the distance ratio of the two nearest neighbors to improve the accuracy of the detection of corresponding keypoint pairs, and it also uses geometric constraint for eliminating incorrect keypoint pairs. Experiments for evaluating the validity the proposed method were performed on MMU public database. The person recognition rate on left and right ocular image datasets showed 91.1% and 90.6% respectively. The performance represents about 5% higher accuracy than the SIFT-based method which has been widely used in a biometric field.

A Study on Korean Isolated Word Speech Detection and Recognition using Wavelet Feature Parameter (Wavelet 특징 파라미터를 이용한 한국어 고립 단어 음성 검출 및 인식에 관한 연구)

  • Lee, Jun-Hwan;Lee, Sang-Beom
    • The Transactions of the Korea Information Processing Society
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    • 제7권7호
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    • pp.2238-2245
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    • 2000
  • In this papr, eatue parameters, extracted using Wavelet transform for Korean isolated worked speech, are sued for speech detection and recognition feature. As a result of the speech detection, it is shown that it produces more exact detection result than eh method of using energy and zero-crossing rate on speech boundary. Also, as a result of the method with which the feature parameter of MFCC, which is applied to he recognition, it is shown that the result is equal to the result of the feature parameter of MFCC using FFT in speech recognition. So, it has been verified the usefulness of feature parameters using Wavelet transform for speech analysis and recognition.

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Semantic-Oriented Error Correction for Voice-Activated Information Retrieval System

  • Yoon, Yong-Wook;Kim, Byeong-Chang;Lee, Gary-Geunbae
    • MALSORI
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    • 제44호
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    • pp.115-130
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    • 2002
  • Voice input is often required in many new application environments, but the low rate of speech recognition makes it difficult to extend its application. Previous approaches were to raise the accuracy of the recognition by post-processing of the recognition results, which were all lexical-oriented. We suggest a new semantic-oriented approach in speech recognition error correction. Through experiments using a speech-driven in-vehicle telematics information application, we show the excellent performance of our approach and some advantages it has as a semantic-oriented approach over a pure lexical-oriented approach.

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An Proposal and Evaluation of the New formant Tracking Algorithm for Speech Recognition (음성인식을 위한 새로운 포만트트랙킹 알고리즘의 제안과 평가)

  • 송정영
    • Journal of Internet Computing and Services
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    • 제3권4호
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    • pp.51-59
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    • 2002
  • For the speech recognition, this paper proposes a improved new formant tracking algorithm The recognition data for the simulation on this paper are used with the Korean digit speech. The recognition rate of the improved algorithm for the Korean digit speech shows 91% for 300 digit speech The effectiveness of this research has been confirmed through recognition simulations.

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Auditory Neural Information Processing Modeling for Speech Recognition (음성인식을 위한 청각신경 정보처리 모델링)

  • Lee, Hee-Kyu;Lee, Kwang-Hyung
    • The Journal of the Acoustical Society of Korea
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    • 제9권3호
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    • pp.42-47
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    • 1990
  • A neural auditory system is studied for the aim of making better speech recognition systems. The cochlear mechanics is described. A IIR digital filter modeling of basilar membrane is discussed for the speech recognition. A multi-layer model of consonant recognition using phoneme detection filters and discriminant functions for feature estimation is constructed. This model shows more then 90% recognition rate in consonants.

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A Vehicle Model Recognition using Car's Headlights Features and Homogeneity Information (차량 헤드라이트 특징과 동질성 정보를 이용한 차종 인식)

  • Kim, Mih-Ho;Choi, Doo-Hyun
    • Journal of Korea Multimedia Society
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    • 제14권10호
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    • pp.1243-1251
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    • 2011
  • This paper proposes a new vehicle model recognition using scale invariant feature transform to car's headlights image. Proposed vehicle model recognition raises the accuracy using "homogeneity" calculated from the distribution of features. In the experiment with 400 test images taken from 54 different vehicles, proposed method has 90% recognition rate and 16.45 homogeneity.