• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.834-842
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• 2015

We propose a method to construct composite feature vector based on discriminant analysis for face recognition. For this, we first extract the holistic- and local-features from whole face images and local images, which consist of the discriminant pixels, by using a discriminant feature extraction method. In order to utilize both advantages of holistic- and local-features, we evaluate the amount of the discriminative information in each feature and then construct a composite feature vector with only the features that contain a large amount of discriminative information. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed composite feature vector has improvement of face recognition performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• 전자공학회논문지
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• 제52권10호
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• pp.129-136
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• 2015

본 논문에서는 조명 변이에 강인하게 동작 할 수 있는 하이브리드 얼굴 인식 방법을 제안한다. 이를 위해, 서로 다른 특성을 가진 조명 불변 특징 추출 방법으로부터 판별력 있는 특징들을 추출한다. 개별 방법들의 장점들을 효과적으로 활용하기 위해, 판별 거리 척도를 이용하여 각 특징들의 분별력을 측정하여 분별력이 높은 특징들로만 복합 특징을 구성하여 얼굴 인식에 사용한다. Multi-PIE, Yale B, AR, yale database들에 대한 실험 결과, 제안한 방법은 모든 database에 대해 개별 조명 불변 특징 방법들보다 우수한 인식 성능을 보여 주었다.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.831-842
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• 1999

We propose a wavelet decomposition feature extraction method for the hand-written character recognition. Comparing the recognition rates of which methods with original image features and with selected features by the wavelet decomposition we study the characteristics of the proposed method. LDA(Linear Discriminant Analysis) QDA(Quadratic Discriminant Analysis) RDA(Regularized Discriminant Analysis) and NN(Neural network) are used for the calculation of recognition rates. 6000 hand-written numerals from CENPARMI at Concordia University are used for the experiment. We found that the set of significantly selected wavelet decomposed features generates higher recognition rate than the original image features.

• 한국지능시스템학회논문지
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• 제19권1호
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• pp.90-95
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• 2009

본 논문은 선형차원감소(Linear Dimensionality Reduction)을 위해 널리 이용되고 있는 특징추출 알고리듬인 선형판별분석(Linear Discriminant Analysis)의 문제점을 해결할 수 있는 새로운 특징추출 알고리듬을 제안한다. 선형판별분석에 포함되는 평균-자료 간 거리 및 평균-평균 간의 거리에 기반한 분산행렬은 역행렬 연산, 계수의 제한 등으로 인하여 계산상의 문제와 추출되는 특징의 수가 제한되는 한계를 가지고 있다. 또한 자료의 집단이 단일 모드의 정규 분포로부터 얻어진 것으로 가정되며 그렇지 않은 경우에 대해서는 적절한 결과를 얻을 수 없다. 본 논문에서는 자료-자료 간의 거리에 기반하고 적절하게 가중치가 추가된 새로운 행렬을 정의하였으며. 이에 기반하여 특징을 추출하는 방법을 제안하였다. 그럼으로써 앞서 선형판별분석의 여러 문제를 해결하고자 시도하였다. 제안된 방법의 성능을 실험을 통해 확인하였다.

• International Journal of Contents
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• 제8권4호
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• pp.56-63
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• 2012

In recent decades, analyzing the activities of human brain achieved some accomplishments by using the functional Magnetic Resonance Imaging (fMRI) technique. fMRI data provide a sequence of three-dimensional images related to human brain's activity which can be used to detect instantaneous cognitive states by applying machine learning methods. In this paper, we propose a new approach for distinguishing human's cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative sentence". Since fMRI data are high dimensional (about 100,000 features in each sample), extremely sparse and noisy, feature selection is a very important step for increasing classification accuracy and reducing processing time. We used the Fisher Discriminant Ratio to select the most powerful discriminative features from some Regions of Interest (ROIs). The experimental results showed that our approach achieved the best performance compared to other feature extraction methods with the average accuracy approximately 95.83% for the first study and 99.5% for the second study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4317-4335
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• 2018

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

• 한국통신학회논문지
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• 제38C권2호
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• pp.208-212
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• 2013

부류안 분산 행렬의 특이성 때문에 선형 판별 분석은 작은 표본 크기 문제에 쓰기에 알맞지 않다. 이에 선형 판별 분석을 확장하여 작은 표본 크기 문제에서 좋은 성능을 갖는 영 공간 기반 선형 판별 분석이 제안되었다. 이 논문에서는 라그랑지 기법을 바탕으로 하여, 영 공간 기반 선형 판별 분석을 써서 특징을 추출하는 문제를 선형 방정식 문제로 바꾸는 과정을 제안하였다.

• 한국멀티미디어학회논문지
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• 제18권9호
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• pp.1039-1046
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• 2015

We propose a feature generation method for low-resolution face recognition. For this, we first generate new features from the input features (pixels) of a low-resolution face image by adding the higher-order terms. Then, we evaluate the separability of both of the original input features and new features by computing the discriminant distance of each feature. Finally, new data sample used for recognition consists of the features with high separability. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed method gives good recognition performance for low-resolution face images compared with other method.

• Journal of International Society for Simulation Surgery
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• 제2권2호
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• pp.64-66
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• 2015

Purpose : In this paper we proposed cascade feature filter and projection method for rapid human face recognition for the large-scale high-dimensional face database. Materials and Methods : The relevant features are selected from the large feature set using Fast Correlation-Based Filter method. After feature selection, project them into discriminant using Principal Component Analysis or Linear Discriminant Analysis. Their cascade method reduces the time-complexity without significant degradation of the performance. Results : In our experiments, the ORL database and the extended Yale face database b were used for evaluation. On the ORL database, the processing time was approximately 30-times faster than typical approach with recognition rate 94.22% and on the extended Yale face database b, the processing time was approximately 300-times faster than typical approach with recognition rate 98.74 %. Conclusion : The recognition rate and time-complexity of the proposed method is suitable for real-time face recognition system on the large-scale high-dimensional face database.