A Novel Kernel Discriminant Analysis for Face Verification

Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. video-video face verification means inputting video setting, verification by using face video data。It has most useful information, including: a lots of pictures of one person, continuity of face in time and space in video, three dimension face model etc. 1.PCA face verification Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. video-video face verification means inputing video setting, verification by using face video data。It has most useful information, including: a lots of pictures of one person, continuity of face in time and space in video, three dimension face model etc. The way that face information be described in video in present literature can be summarized as follows: vector、matrix probability、 dynamic model 、fashion. daopting modes of probability and fashion need mass swatch which reflect distribution of face, can accurately describe face distribution. Dynamic model can better use time and space information , but the method is compicated, calculation is great. The describe way of imputing vector has a big defect, the randomicity of choose swatch.the way of matrix is comparatively simple, can apply the informatin of discontinuous pictures in time,but how to depict relationship between matrix is a worthful contect. Face character based on PCA (Principal Component Analysis, PCA)is the classic arithmetic in face verification, it is a face face veification and describe technology educe from bases analysis. Face character is a simple 、fast、 useful、 arithmetic based on transform coefficient character. Face verification: compare the given face portrait with the picture stored in computer, determine the face is whether the appointed guy, it is a one to one matching process, usually one person stored more face portrait in different angle in computer. A face verification system based on video including face detect model face portrait pretreatment model face character distill model and face verification model. 1.1PCA face verification Face verification: compare the given face portrait with the picture stored in computer, determine the face is whether the appointed guy, it is a one to one matching process, usually one person stored more face portrait in different angle in computer. A face verification system (3) based on video including face detect model face portrait pretreatment model face character distill model and face verification model,as picture 1.

A derivative of Fisher's Linear Discriminant Analysis (FLDA), named Fisherapples for the recognition of apple lesions which is not sensitive to large variations in illumination is proposed in this paper. We make use of the linear projection that is orthogonal to the within-class scatter of the apple images from a high-dimensional image space to a considerably low-dimensional image space. It separates the data-cases well, projecting away variations in lighting. Our approach maximizes the ratio of between-class scatter to that of within-class scatter of apple lesions, i.e., we can get maximal between-class distances and minimal within-class distances after projection. This implies that the gap between the classes becomes bigger and ensures optimal separability in the new space. Besides, we take advantage of Principal Component Analysis (PCA) to project the set of apple images to a lower dimensional space in order to overcome the complication of the singular within-class scatter matrix. After that, the resulting within-class scatter becomes nonsingular and subsequently we can use standard FLDA to reduce the dimension further. Consequently, it is effortless for the computer to calculate the result. Experimental results demonstrate that Fisherapples performs better in apple lesion recognition than PCA.

In this paper, we investigate the application of Fisher’s linear discriminant analysis (FLDA) to hyperspectral remote sensing image classification. The basic idea of FLDA is to design an optimal transform so that the classes can be well separated in the low-dimensional space. The practical difficulty of applying FLDA to hyperspectral images includes the unavailability of enough training samples and unknown information for all the classes present. So the original FLDA is modified to avoid the requirements of complete class knowledge, such as the number of actual classes present. We also investigate the performance of the class of principal component analysis (PCA) techniques prior to FLDA and find that the interference and noise adjusted PCA (INAPCA) can provide the improvement in the final classification.KeywordsFisher’s Linear Discriminant AnalysisDimensionality ReductionClassificationHyperspectral Imagery

In this paper, a novel supervised feature extraction method is presented. The method employs discriminant analysis in the features derived by non-negative matrix factorization (NMF). In this way, a two phase discriminant feature extraction procedure is implemented, namely NMF plus linear discriminant analysis (LDA). The introduced method has been applied to the problem of frontal face verification using the well known XM2VTS database, where a better performance than NMF, eigenfaces and Fisherfaces has been achieved

A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.

The Fisher's linear discriminant analysis (FLDA) is a successful linear feature extraction method which aims to maximize the between-class separability and to minimize the within-class variability. In order to accomplish its goal, FLDA maximizes the Fisher's linear discriminant analysis criterion given in the paper. In this paper we first address the limitations of the classical FLDA criterion and then discuss new criterion functions which were introduced to overcome those limitations. Finally, we demonstrate the capability of the new criteria both theoretically and empirically.

Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. However, FLDA does not employ the geometric distribution weight information of facial images in the training stage. Hence, its recognition accuracy will be affected. In order to enhance the classification power of FLDA method, this paper utilizes radial basis function (RBF) with fractional order to model the geometric distribution weight information of the training samples and proposes a novel geometric distribution weight information based Fisher discriminant criterion. Subsequently, a geometric distribution weight information based LDA (GLDA) algorithm is developed and successfully applied to face recognition. Two publicly available face databases, namely, ORL and FERET databases, are selected for evaluation. Compared with some LDA-based algorithms, experimental results exhibit that our GLDA approach gives superior performance.

Biometric based person identity verification is gaining more and more attention. It has been shown that combining different biometric modalities enables to achieve better performances than single modality. So as to improve the verification accuracy, this paper combines face and fingerprint for person identity verification. And some multimodal biometric information fusion strategies, includes sum rule (SR), weighted sum rule (WSR), Fisher linear discriminant analysis (FLDA) and support vector machine (SVM) are evaluated, furthermore, a new method for data normalization in verification system is proposed in this paper. Experiment results prove the effectiveness of fusion of multiple biometrics compared with single biometric, and also the better verification performance by adopting the new data normalization method. The SVM, SR, WSR and FLDA fusion methods present a decreasing performance in our experiment.

This paper presents a new method for similarity metric learning, called Logistic Similarity Metric Learning (LSML), where the cost is formulated as the logistic loss function, which gives a probability estimation of a pair of faces being similar. Especially, we propose to shift the similarity decision boundary gaining significant performance improvement. We test the proposed method on the face verification problem using four single face descriptors: LBP, OCLBP, SIFT and Gabor wavelets. Extensive experimental results on the LFW-a data set demonstrate that the proposed method achieves competitive state-of-the-art performance on the problem of face verification.

In this work, we investigate the recognition of human emotional states from audiovisual signals. We extract prosodic, Mel-frequency Cepstral Coeffieient (MFCC), and formant frequency features to represent the audio characteristic of the emotional speech. A face detection scheme based on HSV color model is used to detect the face from the background. The facial expressions are represented by Gabor wavelet features. We perform feature selection by using the stepwise method based on Mahalanobis distance. The selected features are used to classify the emotional data into their corresponding classes. Different classification algorithms including Gaussian Mixture Model (GMM), K-nearest neighbours(K-NN), Neural Network (NN), and Fisher's Linear Discriminant Analysis (FLDA) are compared in this study. An adaptive multi-classifier scheme involving the analysis of individual class and combinations of different classes is proposed. Our recognition system is tested over a language independent database. The proposed FLDA-based multi-classifier scheme achieves the best overall and individual class recognition accuracy.

In this work, we investigate the recognition of human emotional states from audiovisual signals. We extract prosodic, Mel-frequency Cepstral Coeffieient (MFCC), and formant frequency features to represent the audio characteristic of the emotional speech. A face detection scheme based on HSV color model is used to detect the face from the background. The facial expressions are represented by Gabor wavelet features. We perform feature selection by using the stepwise method based on Mahalanobis distance. The selected features are used to classify the emotional data into their corresponding classes. Different classification algorithms including Gaussian Mixture Model (GMM), K-nearest neighbours(K-NN), Neural Network (NN), and Fisher's Linear Discriminant Analysis (FLDA) are compared in this study. An adaptive multi-classifier scheme involving the analysis of individual class and combinations of different classes is proposed. Our recognition system is tested over a language independent database. The proposed FLDA-based multi-classifier scheme achieves the best overall and individual class recognition accuracy.

Subspace analysis is an effective technique for dimensionality reduction, which aims at finding a low-dimensional space of high-dimensional data. Fisher linear discriminant analysis (FLD) and Neural Networks are commonly used techniques of image processing and recognition. In this paper, a new scheme of image feature extraction namely, the FLD and Generalized Regression Neural Networks (GRNN) approach for image recognition is presented. Linear discriminant analysis is usually performed to investigate differences among multivariate classes, to determine which attributes discriminate between the classes, and to determine the most parsimonious way to distinguish among classes. Two-dimensional linear discriminative features and GRNN are used to perform the classification. Experiments on the image database (Face, Object and Character) demonstrate the effectiveness and feasibility of the proposed method.

An algorithm for gamma-ray identification applications has been developed and evaluated. The algorithm is based on a Fisher Linear Discriminant Analysis (FLDA) technique that generates loading coefficients that maximize the separation of a particular radionuclide from all the other radionuclides in a training library. Separate experimental data sets were obtained for the algorithms training data and for the performance evaluation. The algorithm was evaluated against a range of radionuclides and acquisitions times. An inverse square root relationship between the cluster standard deviation and its gross mean counts enabled the production of an adaptable threshold. The inverse square relationship between the Mahalanobis distance metric and the 137Cs standoff distance demonstrated a means to quantify the measured number of counts. The FLDA identification performance, for a number of threat radionuclides (including special nuclear materials), exceeded that of a commercially available peak search algorithm. The high sensitivity and specificity, of the FLDA algorithm, was maintained in low count situations. The poor performance for some radionuclides was attributed to the measured number of counts being below the minimal detectable limit. The FLDA algorithm has the potential to be used in gamma-ray identification applications and, in particular, count starved situations.

Land degradation (LD) is the decline in a land's functional capacity and productive potential, which includes various anthropogenic and natural drivers. This study focuses on three primary manifestations of LD including soil erosion, landslides, and rockfalls, which are the most prevalent in the Shaqlawa district. A set of 22 LD conditioning factors, encompassing curvature, lithology, aspect, river density, soil type, lineament density, river distance, elevation, road distance, length slope (LS), land use land cover (LULC), stream power index (SPI), valley depth, profile curvature, slope, solar radiation, road density, lineament distance, rainfall, topographic wetness index (TWI), plan curvature, and normalized difference vegetation index (NDVI), were integrated into the analysis. Variance inflation factors (VIF) and tolerance (TOL) values from linear regression indicate that most LD factors have acceptable levels of multicollinearity. The Information Gain Ratio (IGR) identified key variables TWI, NDVI, and lithology-as pivotal factors for predicting LD. Additionally, the study evaluated degradation factors using various machine learning (ML) algorithms, including random forest (RF), Naive Bayes, logistic regression, rotation forest, forest penalized attributes (FPA), and Fisher's Linear discriminant analysis (FLDA). This facilitated categorizing the study area into five susceptibility categories. The FLDA model categorized the highest area under very high degradation risk at 26.72%, emphasizing the varied insights each algorithm brought to characterizing the degradation risk. Additionally, the receiver operating characteristic curves (ROC) were employed for model validation, identifying RF as the most successful model in the training dataset with an area under the curve (AUC) of 0.882, while FLDA outperformed in the testing dataset with an AUC of 0.883. The identified LD-prone areas will help land-use planners and emergency management officials apply effective mitigation strategies for similar terrains.

Driver’s condition plays a critical role in driving safety. The fact that about 20 percent of automobile accidents occurred due to driver fatigue leads to a demand for developing a method to monitor driver’s status. In this study, we acquired brain signals such as oxy- and deoxyhemoglobin and neuronal electrical activity by a hybrid fNIRS/EEG system. Experiments were conducted with 11 subjects under two conditions: Normal condition, when subjects had enough sleep, and sleep deprivation condition, when subject did not sleep previous night. During experiment, subject performed a driving task with a car simulation system for 30 minutes. After experiment, oxy-hemoglobin and deoxy-hemoglobin changes were derived from fNIRS data, while beta and alpha band relative power were calculated from EEG data. Decrement of oxy-hemoglobin, beta band power, and increment of alpha band power were found in sleep deprivation condition compare to normal condition. These features were then applied to classify two conditions by Fisher’s linear discriminant analysis (FLDA). The ratio of alpha-beta relative power showed classification accuracy with a range between 62% and 99% depending on a subject. However, utilization of both EEG and fNIRS features increased accuracy in the range between 68% and 100%. The highest increase of accuracy is from 63% using EEG to 99% using both EEG and fNIRS features. In conclusion, the enhancement of classification accuracy is shown by adding a feature from fNIRS to the feature from EEG using FLDA which provides the need of developing a hybrid fNIRS/EEG system.