Robust authentication using the unconstrained infrared face images

Face recognition under the unconstrained conditions that exist in surveillance is the need of the present times. Thus for high end security the research on IR based face recognition assumes importance because of its insensitivity to illumination, disguise and surgery. This paper presents IR face based biometric authentication using the information-set based four types of interactive features and two classifiers. The information sets originate from a fuzzy set on representing the uncertainty associated with the information source instead of a membership function which gives only the degree of association to the fuzzy set. The four feature types include the effective exponential information source (EEI), the effective Gaussian information source (EGI), the effective multi quadratic information source (EMQDI) and inverse of this feature (EIMQDI). The interactive features are obtained by taking the s-norms on the features from the successive windows. Two classifiers called the Hanman Classifier and the weighted Hanman Classifier are formulated using t-norms. The features and classifiers are tested on the created databases incorporating the unconstrained conditions such as occlusion, less resolution and noise.     

Enhancing decision combination of face and fingerprint by exploitation of individual classifier space: An approach to multi-modal biometry

This paper presents a new approach to combine decisions from face and fingerprint classifiers for multi-modal biometry by exploiting the individual classifier space on the basis of availability of class-specific information present in the classifier space. We exploit the prior knowledge by training the face classifier using response vectors on a validation set, enhancing class separability (using parametric and nonparametric Linear Discriminant Analysis) in the classifier output space and thereby improving the performance of the face classifier. Fingerprint classifier often does not provide this information due to high sensitivity of available minutiae points, producing partial matches across subjects. The enhanced face and fingerprint classifiers are combined using a sum rule. We also propose a generalized algorithm for multiple classifier combination (MCC) based on our approach. Experimental results show superiority of the proposed method over other existing fusion techniques, such as sum, product, max, min rules, decision template and Dempster–Shafer theory.     

Support vector learning for fuzzy rule-based classification systems

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.     

Face recognition under pose and illumination variations using the combination of Information set and PLPP features

This paper presents a new approach for face recognition under pose and illumination variations. The concept of information set is presented and the features based on this are derived using the Mamta-Hanman entropy function. The properties of an adaptive version of this entropy are given and nonlinear Shannon transform and Hanman transform which area higher form of information set are formulated. The information set based features and the nonlinear Shannon transform features are separately combined with the Pseudo-inverse Locality Preserving Projections (PLPP) for improving their effectiveness. The performance of the combined features is compared with that of the holistic approaches on four face databases (two FERET, one head pose image, and Extended Yale face database). The features from the combination of nonlinear Shannon transform and PLPP give consistent performance on the three databases tested whereas the well known features from the literature show good performance on one or two databases only.     

基于类别信息的分类器集成方法Cagging

提出一种基于类别信息的分类器集成方法Cagging.基于类别信息重复选择样本生成基本分类器的训练集,增强了基本分类器之间的差异性;利用基本分类器对不同模式类的分类能力为每个基本分类器设置一组权重.使用权重对各分类器输出结果进行加权决策,较好地利用了各个基本分类器之间的差异性.在人脸图像库ORL上的实验验证了Cagging的有效性.此外,Cagging方法的基本分类器生成方式适合于通过增量学习生成集成分类器,扩展Cagging设计了基于增量学习的分类器集成方法Cagging-Ⅰ,实验验证了它的有效性.     

Fuzzy classifier design using genetic algorithms

A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed. The optimal parameters of the fuzzy classifier including fuzzy membership functions and the size and structure of fuzzy rules are extracted from the training data using GAs. This is done by introducing new representation schemes for fuzzy membership functions and fuzzy rules. An effectiveness measure for fuzzy rules is developed that allows for systematic addition or deletion of rules during the GA optimization process. A clustering method is utilized for generating new rules to be added when additions are required. The performance of the classifier is tested on two real-world databases (Iris and Wine) and a simulated Gaussian database. The results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules. The performance is also compared to other fuzzy classifiers tested on the same databases.     

Confidence factor weighted Gaussian function induced parallel fuzzy rank-level fusion for inference and its application to face recognition

This paper proposes a novel approach for inference using fuzzy rank-level fusion and explores it application to face recognition using multiple biometric representations. Multiple representations of single biometric (trait) aim to increase the reliability or acceptance of a biometric system, as it exploits the underlying essential characteristics provided by different sensors. In this paper, we propose a new scheme for generating fuzzy ranks induced by a Gaussian function based on the confidence of a classifier. In contrast to the conventional ranking, this fuzzy ranking reflects some associations among the outputs (confidence factors) of a classifier. These fuzzy ranks, yielded by multiple representations of a face image, are fused weighted by the corresponding confidence factors of the classifier to generate the final ranks while recognizing a face. In many real-world applications, where multiple traits of a person are unavailable, the proposed method is highly effective. However, it can easily be extended to multimodal biometric systems utilizing multiple classifiers. The experimental results using different feature vectors of a face image employing different classifiers show that the proposed method can significantly improve recognition accuracy as compared to those from individual feature vectors and as well as some commonly used rank-level fusion methods.     

ID3-derived fuzzy rules and optimized defuzzification forhandwritten numeral recognition

Presents a technique to produce fuzzy rules based on the ID3 approach and to optimize defuzzification parameters by using a two-layer perceptron. The technique overcomes the difficulties in a conventional syntactic approach to handwritten character recognition, including problems of choosing a starting or reference point, scaling, and learning by machines. The authors' technique provides: a way to produce meaningful and simple fuzzy rules; a method to fuzzify ID3-derived rules to deal with uncertain, noisy, or fuzzy data; and a framework to incorporate fuzzy rules learned from the training data and those extracted from human recognition experience. The authors' experimental results on NIST Special Database 3 show that the technique out-performs the straight forward ID3 approach. Moreover, ID3-derived fuzzy rules can be combined with an optimized nearest neighbor classifier, which uses intensity features only, to achieve a better classification performance than either of the classifiers. The combined classifier achieves a correct classification rate of 98.6% on the test set     

Constructing L2-SVM-Based Fuzzy Classifiers in High-Dimensional Space With Automatic Model Selection and Fuzzy Rule Ranking

In this paper, a new scheme for constructing parsimonious fuzzy classifiers is proposed based on the L2-support vector machine (L2-SVM) technique with model selection and feature ranking performed simultaneously in an integrated manner, in which fuzzy rules are optimally generated from data by L2-SVM learning. In order to identify the most influential fuzzy rules induced from the SVM learning, two novel indexes for fuzzy rule ranking are proposed and named as alpha-values and omega-values of fuzzy rules in this paper. The alpha-values are defined as the Lagrangian multipliers of the L2-SVM and adopted to evaluate the output contribution of fuzzy rules, while the omega-values are developed by considering both the rule base structure and the output contribution of fuzzy rules. As a prototype-based classifier, the L2-SVM-based fuzzy classifier evades the curse of dimensionality in high-dimensional space in the sense that the number of support vectors, which equals the number of induced fuzzy rules, is not related to the dimensionality. Experimental results on high-dimensional benchmark problems have shown that by using the proposed scheme the most influential fuzzy rules can be effectively induced and selected, and at the same time feature ranking results can also be obtained to construct parsimonious fuzzy classifiers with better generalization performance than the well-known algorithms in literature.     

Knowledge acquisition in incomplete fuzzy information systems via the rough set approach

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.     

A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines

This paper proposes a classification method that is based on easily interpretable fuzzy rules and fully capitalizes on the two key technologies, namely pruning the outliers in the training data by SVMs (support vector machines), i.e., eliminating the influence of outliers on the learning process; finding a fuzzy set with sound linguistic interpretation to describe each class based on AFS (axiomatic fuzzy set) theory. Compared with other fuzzy rule-based methods, the proposed models are usually more compact and easily understandable for the users since each class is described by much fewer rules. The proposed method also comes with two other advantages, namely, each rule obtained from the proposed algorithm is simply a conjunction of some linguistic terms, there are no parameters that are required to be tuned. The proposed classification method is compared with the previously published fuzzy rule-based classifiers by testing them on 16 UCI data sets. The results show that the fuzzy rule-based classifier presented in this paper, offers a compact, understandable and accurate classification scheme. A balance is achieved between the interpretability and the accuracy.     

Neutrosophic classifier: An extension of fuzzy classifer

Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capable of effectively handling indeterminacy, stochasticity acquisition errors that fuzzy logic cannot handle. The proposed neutrosophic classifier employs neutrosophic logic for its working and is an extension of commonly used fuzzy classifier. It is compared with the commonly used fuzzy classifiers on the following parameters: nature of membership functions, number of rules and indeterminacy in the results generated. It is proved in the paper that extended fuzzy classifier: neutrosophic classifier; optimizes the said parameters in comparison to the fuzzy counterpart. Finally the paper is concluded with justifying that neutrosophic logic though in its nascent stage still holds the potential to be experimented for further exploration in different domains.     

How good are fuzzy If-Then classifiers?

This paper gives some known theoretical results about fuzzy rule-based classifiers and offers a few new ones. The ability of Takagi-Sugeno-Kang (TSK) fuzzy classifiers to match exactly and to approximate classification boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a classification boundary in R (2) is extended from monotonous to arbitrary functions. Equivalence between fuzzy rule-based and nonfuzzy classifiers (1-nn and Parzen) is outlined. We specify the conditions under which a class of fuzzy TSK classifiers turn into lookup tables. It is shown that if the rule base consists of all possible rules (all combinations of linguistic labels on the input features), the fuzzy TSK model is a lookup classifier with hyperbox cells, regardless of the type (shape) of the membership functions used. The question "why fuzzy?" is addressed in the light of these results.     

A complete fuzzy discriminant analysis approach for face recognition

In this paper, some studies have been made on the essence of fuzzy linear discriminant analysis (F-LDA) algorithm and fuzzy support vector machine (FSVM) classifier, respectively. As a kernel-based learning machine, FSVM is represented with the fuzzy membership function while realizing the same classification results with that of the conventional pair-wise classification. It outperforms other learning machines especially when unclassifiable regions still remain in those conventional classifiers. However, a serious drawback of FSVM is that the computation requirement increases rapidly with the increase of the number of classes and training sample size. To address this problem, an improved FSVM method that combines the advantages of FSVM and decision tree, called DT-FSVM, is proposed firstly. Furthermore, in the process of feature extraction, a reformative F-LDA algorithm based on the fuzzy k-nearest neighbors (FKNN) is implemented to achieve the distribution information of each original sample represented with fuzzy membership grade, which is incorporated into the redefinition of the scatter matrices. In particular, considering the fact that the outlier samples in the patterns may have some adverse influence on the classification result, we developed a novel F-LDA algorithm using a relaxed normalized condition in the definition of fuzzy membership function. Thus, the classification limitation from the outlier samples is effectively alleviated. Finally, by making full use of the fuzzy set theory, a complete F-LDA (CF-LDA) framework is developed by combining the reformative F-LDA (RF-LDA) feature extraction method and DT-FSVM classifier. This hybrid fuzzy algorithm is applied to the face recognition problem, extensive experimental studies conducted on the ORL and NUST603 face images databases demonstrate the effectiveness of the proposed algorithm.     

Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump

Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the FIS performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers.     

MPEG VBR video traffic modeling and classification using fuzzytechnique

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters     

Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space

An evolutionary approach to designing accurate classifiers with a compact fuzzy-rule base using a scatter partition of feature space is proposed, in which all the elements of the fuzzy classifier design problem have been moved in parameters of a complex optimization problem. An intelligent genetic algorithm (IGA) is used to effectively solve the design problem of fuzzy classifiers with many tuning parameters. The merits of the proposed method are threefold: 1) the proposed method has high search ability to efficiently find fuzzy rule-based systems with high fitness values, 2) obtained fuzzy rules have high interpretability, and 3) obtained compact classifiers have high classification accuracy on unseen test patterns. The sensitivity of control parameters of the proposed method is empirically analyzed to show the robustness of the IGA-based method. The performance comparison and statistical analysis of experimental results using ten-fold cross validation show that the IGA-based method without heuristics is efficient in designing accurate and compact fuzzy classifiers using 11 well-known data sets with numerical attribute values.     

Higher order information set based features for text-independent speaker identification

In this paper Type-2 Information Set (T2IS) features and Hanman Transform (HT) features as Higher Order Information Set (HOIS) based features are proposed for the text independent speaker recognition. The speech signals of different speakers represented by Mel Frequency Cepstral Coefficients (MFCC) are converted into T2IS features and HT features by taking account of the cepstral and temporal possibilistic uncertainties. The features are classified by Improved Hanman Classifier (IHC), Support Vector Machine (SVM) and k-Nearest Neighbours (kNN). The performance of the proposed approaches is tested in terms of speed, computational complexity, memory requirement and accuracy on three datasets namely NIST-2003, VoxForge 2014 speech corpus and VCTK speech corpus and compared with that of the baseline features like MFCC, ?MFCC, ??MFCC and GFCC under white Gaussian noisy environment at different signal-to-noise ratios. The proposed features have the reduced feature size, computational time, and complexity and also their performance is not degraded under the noisy environment.     

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed–crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed–crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naïve Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed.