A linear discriminant analysis using weighted local structure information

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

Many state-of-the-art i-vector based voice biometric systems use linear discriminant analysis (LDA) as a post-processing stage to increase the computational efficiency in the back-end via dimensionality reduction, as well as annihilate the undesired (noisy) directions in the total variability subspace. The traditional approach for computing the LDA transform uses parametric representations for both intra- and inter-class scatter matrices that are based on the Gaussian distribution assumption. However, it is known that the actual distribution of i-vectors may not necessarily be Gaussian, and in particular, in the presence of noise and channel distortions. In addition, the rank of the LDA projection (i.e., the maximum number of available discriminant bases) is limited to the number of classes minus 1. Accordingly, language recognition tasks on noisy data that involve only a few language classes receive limited or no benefit from the LDA post-processing. Motivated by this observation, we present an alternative non-parametric discriminant analysis (NDA) technique that measures both the within- and between-language variation on a local basis using the nearest neighbor rule. The effectiveness of the NDA method is evaluated in the context of noisy language recognition tasks using speech material from the DARPA Robust Automatic Transcription of Speech (RATS) program. Experimental results indicate that NDA is more effective than the traditional parametric LDA for language recognition under noisy and channel degraded conditions.

Employing fisher discriminant analysis for Arabic text classification

Exploring sex classification from earprints - A comparison of supervised machine learning algorithms and conventional linear discriminant analysis.

Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces onto which these samples are projected. Furthermore, both methods have been successfully applied to face recognition. Although LDA and MPCA share common goals and methodologies, in previous research they have been applied separately and independently. In this paper, we propose an extension of LDA to multiple factor frameworks. Our proposed method, Multifactor Discriminant Analysis, aims to obtain multilinear projections that maximize the between-class scatter while minimizing the withinclass scatter, which is the same core fundamental objective of LDA. Moreover, Multifactor Discriminant Analysis (MDA), like MPCA, uses multifactor analysis and calculates subject parameters that represent the characteristics of subjects and are invariant to other changes, such as viewpoints or lighting conditions. In this way, our proposed MDA combines the best virtues of both LDA and MPCA for face recognition.

This is the author’s version of a work that was submitted/accepted for pub-lication in the following source:

In the biometrics community, face and speaker recognition are mature fields in which several systems have been proposed over the past twenty years. While existing systems perform well under controlled recording conditions, mismatch caused by the use of different sensors or a lack of cooperation from the subject still significantly affects performance, especially in challenging scenarios such as in forensics. Furthermore, existing methods suffer from scalability issues, which prevents them from taking advantage of increasingly large amounts of training data. This is otherwise a promising approach to improve accuracy in such challenging scenarios. In this thesis we address these problems of mismatch and complexity by developing scalable probabilistic models that we apply to face, speaker and bimodal recognition. Our contributions are four-fold. First, we propose a unified framework for session variability modeling techniques based on Gaussian mixture models (GMM), that encompasses inter-session variability (ISV) modeling, joint factor analysis (JFA) and total variability (TV) modeling. Second, we propose a novel exact and scalable formulation of probabilistic linear discriminant analysis (PLDA), which is a probabilistic and generative framework that models between-class and within-class variations. This formulation solves a major scalability issue, by improving both the time complexity of the training procedure from cubic to linear with respect to the number of samples per class, and the complexity of the scoring procedure. Furthermore, the implementations of all the proposed techniques are integrated into a novel collaborative open source software library called Bob 1 that enforces fair evaluations and encourages reproducible research. Fourth and finally, large-scale experiments are conducted with all of the above machine learning algorithms on several databases such as FRGC for face recognition, NIST SRE12 for speaker recognition and MOBIO for bimodal recognition, showing competitive performances.

Principle Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are known as classical techniques used in face recognition. Both 2-D PCA, 2-D LDA are based on 2-D matrices as opposed to classical PCA and LDA which are based on 1-D vectors. In current work Discriminative Component Analysis(DCA) simultaneous projection of probe image into PCA and LDA face spaces for face recognition is proposed. Also Alternate DCA is proposed. The study is conducted on AT&T database (formerly ORL database) and study reports are encouraging as that of other analysis.

Because the Small Sample Size (SSS) problem usually leads to singularity of the within-class scatter matrix,there will be an ill-posed problem in solving the generalized character equation. An improved algorithm based on former ones was proposed. It introduced mirror image to enlarge sample capacity and adopted the method of the null space of the within-class scatter matrix S w to get the best solution of Fisher criterion. Experimental results on ORL face database and Yale face database show that the proposed algorithm is more effective than traditional Linear Discriminant Analysis (LDA),Independent Components Analysis (ICA) and 2-Dimensional Symmetric Principal Component Analysis (2DSPCA).

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

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.

In this paper, a robust face recognition algorithm is proposed, which is based on the elastic graph matching (EGM) and discriminative feature analysis algorithm. We introduce a cost function for the EGM taking account of variations in face pose and facial expressions, and propose its optimization procedure. Our proposed cost function uses a set of Gabor-wavelet-based features, called robust jet, which are robust against the variations. The robust jet is defined in terms of discrete Fourier transform coefficients of Gabor coefficients. To cope with the difference between face poses of test face and reference faces, 2 x 2 warping matrix is incorporated in the proposed cost function. For the discriminative feature analysis, linear projection discriminant analysis and kernel-based projection discriminant analysis are introduced. These methods are motivated to solve the small-size problem of training samples. The basic idea of PDA is that a class is represented by a subspace spanned by some training samples of the class instead of using sample mean vector, that the distance from a pattern to a class is defined by using the error vector between the pattern and its projection to the subspace representing the class, and that an optimum feature selection rule is developed using the distance concept in a similar way as in the conventional linear discriminant analysis. In order to evaluate the performance of our face recognition algorithm, we carried out some experiments using the well-known FERET face database, and compared the performance with recently developed approaches. We observed that our algorithm outperformed the compared approaches.

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

Existing supervised learning models are generally built upon the basis of only one single objective function, through the minimizing of the square-loss (neural networks) or the minimizing of the information entropy (decision tree). Due to the inherent complexity of the real-life data, learning models merely based on only one single objective function are always inadequate. Consequently, many well-known classification models adopt multiple objective optimization to guide the learning process. For example, Fisherpsilas linear discriminant analysis (LDA) is built by maximizing the ldquobetween-class variancerdquo (the first objective function) and minimizing the ldquowithin-class variancerdquo (the second objective function); SVM is built by maximizing the ldquomarginal distancerdquo (the first objective function) and minimizing the ldquoerror distancerdquo (the second objective function). In this paper, we combine Fisherpsilas LDA (maximizing the ldquobetween-class variancerdquo) measure and SVMpsilas minimizing the ldquoerror distancerdquo measure to formulate a new multiple objective classification model, namely minimal error and maximal between-class variance (MEMBV) model. Experimental results demonstrate the performance of the proposed new model on synthetic and real-life datasets.

Linear Discriminant Analysis (LDA) has been successfully applied to face recognition which is based on a linear projection from the image space to a low dimensional space by maximizing the between class scatter and minimizing the within-class scatter. LDA allows objective evaluation of the significance of visual information in different features of the face for identifying the human face. The LDA also provides us with a small set of features that carry the most relevant information for classification purposes. LDA method overcomes the limitation of Principle Component Analysis method by applying the linear discriminant criterion. This criterion tries to maximize the ratio of determinant of the between-class scatter matrix of the projected samples to the determinant of the within-class scatter matrix of the projected samples. Linear discriminant groups the images of the same class and separate images of different classes. Here to identify an input test image, the projected test image is compared to each projected training, and the test image is identified as the closest training image. The experiments in this paper we present to use LDA for face recognition. The experiments in this paper are performed with the ORL face database. The experimental results show that the correct recognition rate of this method is higher than that of previous techniques.