Face Recognition Using Boosted Regularized Linear Discriminant Analysis

Fisher linear discriminant analysis (LDA) involves within-class and between-class covariance matrices. For 2-D data such as images, regularized LDA (RLDA) can improve LDA due to the regularized eigenvalues of the estimated within-class matrix. However, it fails to consider the eigenvectors and the estimated between-class matrix. To improve these two matrices simultaneously, we propose in this paper a new two-stage method for 2-D data, namely a bidirectional LDA (BLDA) in the first stage and the RLDA in the second stage, where both BLDA and RLDA are based on the Fisher criterion that tackles correlation. BLDA performs the LDA under special separable covariance constraints that incorporate the row and column correlations inherent in 2-D data. The main novelty is that we propose a simple but effective statistical test to determine the subspace dimensionality in the first stage. As a result, the first stage reduces the dimensionality substantially while keeping the significant discriminant information in the data. This enables the second stage to perform RLDA in a much lower dimensional subspace, and thus improves the two estimated matrices simultaneously. Experiments on a number of 2-D synthetic and real-world data sets show that BLDA+RLDA outperforms several closely related competitors.

A new face recognition method is proposed in this paper. The proposed method is based on fuzzy regularized linear discriminant analysis (FR-LDA) and combines the regularized linear discriminant analysis (R-LDA) and the fuzzy set theory. R-LDA is based on a new regularized Fisher's discriminant criterion, which is particularly robust against the small sample size problem compared to the traditional one used in LDA. In the proposed method, we calculate the membership degree matrix by Fuzzy K-nearest neighbor (FKNN) and then incorporate the membership degree into the definition of the between-class and within-class scatter matrices and get the fuzzy between-class and within-class scatter matrices. Experimental results obtained on the FERET database demonstrate that the proposed method improves the classification rate performance.

Although mostly used for pattern classification, linear discriminant analysis (LDA) may also be used for feature selection. When employed to select genes for microarray data, which has high dimensionality and small sample size, LDA encounters three problems, including singularity of scatter matrix, overfitting and prohibitive computational complexity. In this study, we propose a new regularization technique to address the singularity and overfitting problem. In addition, we develop a recursive implementation for LDA to reduce computational overhead. Experimental studies on 5 gene microarray problems show that the regularized linear discriminant analysis (RLDA) and its recursive implementation produce gene subsets with excellent classification performance.

Linear Discriminant Analysis (LDA) has been one of the popular subspace methods for face recognition. But this method suffers from the small sample size (SSS) problem, also known as 'curse of dimensionality'. Various techniques have been proposed in literature to overcome this limitation. But it is still unclear which method provides the best solution to SSS problem. In this paper, we have investigated the performance of some popular subspace methods such as principal component analysis (PCA), PCA + LDA, LDA via QR decomposition, Null-space LDA, Exponential Discriminant Analysis (EDA), PCA+EDA etc. Extensive experiments have been performed on five publically available face datasets viz. AR, CMU-PIE, PIX, Yale and YaleB. The performance is measured in terms of average classification accuracy. Experimental results show that the performance increases with the increase in the number of images per person in training set irrespective of the datasets. There is no clear winner among the subspace methods under investigation. But, the performance of PCA+LDA and PCA+EDA is consistent in tackling SSS problem irrespective of the dataset and can also handle the illumination variation in face recognition.

A novel feature dimensionality reduction strategy based on kernel regularized relevance weighted discriminant analysis is proposed in this paper with some interesting characteristics. First, the proposed method has shown its effectiveness in dealing with a small sample size problem when using the regularized linear discriminant analysis (RLDA) technique and Kernel theory. Second, a new computation method is proposed to solve the complicated and inefficient computation procedure problem in the traditional RLDA technique while using the cross-validation method. The experimental results indicate that the proposed algorithm shows better performance than the other methods.

Face recognition based on multi-class mapping of Fisher scores

A novel feature generation scheme which combines multiclass mapping of Fisher scores and appearance based features for face recognition (FR) is proposed in this paper. Multi-class mapping of Fisher scores is based on partial derivative analysis of parameters of hidden Markov model (HMM), and appearance based features are obtained directed from face images. Linear discriminant analysis (LDA) is used to analyze the feature vectors generated under this scheme. Recognition performance improvement is observed over stand-alone HMM method as well as Fisherface method, which also uses appearance based feature vectors. Moreover, by reducing the number of models involved in the training and testing stages, the proposed feature generation scheme can maintain very high discriminative power at much lower computational complexity comparing to that of the traditional HMM based FR system. Experimental results are provided to demonstrate the viability of this scheme for face recognition.

In this paper, we propose a multi-modal biometrics system based on the face and signature recognition. For this, we suggest biometric algorithms for the face and signature recognition. First, we describe a fuzzy linear discriminant analysis (LDA) method for the face recognition. It is an expanded version of the Fisherface method using the fuzzy logic which assigns fuzzy membership to the LDA feature values. On the other hand, the signature recognition has the problem that its performance is often deteriorated by signature variation from various factors. Therefore, we propose a robust online signature recognition method using LDA and so-called Partition Peak Points (PPP) matching technique. Finally, we propose a fusion method for multi-modal biometrics based on the support vector machine. From the various experiments, we find that the proposed method renders higher recognition rates comparing with the single biometric cases under various situations.KeywordsSupport Vector MachineFeature VectorFace RecognitionLinear Discriminant AnalysisFace ImageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper proposes an automatic stream-weight optimization method for noise-robust speaker verification using multi-stream HMMs integrating spectral and prosodic information. The paper first shows the effectiveness of the multi-stream technique in our speaker verification framework. Next, a stream-weight adaptation method combining the linear discriminant analysis (LDA) and Adaboost techniques is proposed. Experiments were conducted using four-connected-digit utterances of Japanese contaminated by white noise with various SNRs. Experimental results show that 1) the verification performance was improved in all SNR conditions by using stream weights estimated by the LDA and 2) the performance is further improved by using the Adaboost in 10 - 30dB SNR conditions.

Face recognition using direct, weighted linear discriminant analysis and modular subspaces

Linear Discriminant Analysis (LDA) is a popular statistical method for both feature extraction and dimensionality reduction in face recognition. The major drawback of LDA is the so-called small sample size (3S) problem. This problem always occurs when the total number of training samples is smaller than the dimension of feature space. Under this situation, the within-class scatter matrix Sw becomes singular and LDA approach cannot be implemented directly. To overcome the 3S problem, this paper proposes a novel wavelet-face based subspace LDA algorithm. Wavelet-face feature extraction and dimensionality reduction are based on two-level D4-filter wavelet transform and discarding the null space of total class scatter matrix St. It is shown that our obtained projection matrix satisfies the uncorrelated constraint conditions. Hence in the sense of statistical uncorrelation, this projection matrix is optimal. The proposed method for face recognition has been evaluated with two public available databases, namely ORL and FERET databases. Comparing with existing LDA-based methods to solve the 3S problem, our method gives the best performance.

Background The diagnosis of chronic rhinosinusitis (CRS) is a complicated procedure. An electronic nose (eNose) is a novel method that detects disease from gas-phase mixtures, such as human breath. Aims/Objectives To determine whether an eNose based on differential mobility spectrometry (DMS) can detect chronic rhinosinusitis without nasal polyps (CRSsNP) by analyzing aspirated nasal air. Materials and methods Adult patients with CRSsNP were examined. The control group consisted of patients with septal deviation. Nasal air was aspirated into a collection bag and analyzed with DMS. The DMS data were classified using regularized linear discriminant analysis (LDA) models with 10-fold cross-validation. Results The accuracy of the DMS to distinguish CRSsNP from patients with septal deviation was 69%. Sensitivity and specificity were 67 and 70%, respectively. Bonferroni-corrected statistical differences were clearly noted. When a subgroup with more severe inflammatory disease was compared to controls, the classification accuracy increased to 82%. Conclusions The results of this feasibility study demonstrate that CRSsNP can potentially be differentiated distinguished from patients with similar nasal symptoms by analyzing the aspirated nasal air using DMS. Further research is warranted to evaluate the ability of this novel method in the differential diagnostics of CRS.

Face recognition using direct, weighted linear discriminant analysis and modular subspaces

A novel Bayesian logistic discriminant model: An application to face recognition

In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural network classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.