Combination of Warping Robust Elastic Graph Matching and Kernel-Based Projection Discriminant Analysis for Face Recognition

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.

A major and demanding issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who is attrite from one Telecom service provider to competitors searching for better services offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial to a company than gain newly recruited customers. Researchers and practitioners are paying great attention and investing more in developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest approach for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) as a classifier in the construction of PPForest to differentiate between churners and non-churners customers. The second method is a Linear Discriminant Analysis (LDA) to achieve linear splitting of variables node during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Gini coefficient, Kolmogorov-Smirnov statistic and lift coefficient, H-measure, AUC. Moreover, PPForest based on direct applied of LDA on the raw data delivers an effective evaluator for the customer churn prediction model.

In this paper, we investigate the use of discriminant techniques in the elastic graph matching (EGM) algorithm. First we use discriminant analysis in the feature vectors of the nodes in order to find the most discriminant features. The similarity measure for discriminant feature vectors and the node deformation are combined in a discriminant manner in order to form a local similarity measure between nodes. Moreover, the local similarity values at the nodes of the elastic graph, are weighted by coefficients that are also derived by some discriminant analysis in order to form a total similarity measure between faces. We illustrate the improvements in performance in frontal face verification using a modified multiscale morphological analysis.

Face recognition at a distance is one grand challenge for security surveillance. In this paper, the face images at different distances are simulated by varying image scales (resolutions). The performances of three face recognition algorithms (matchers) are tested with variant image scales (simulating different distances) and with two spectral images (modalities). The three selected matchers are face pattern byte, elastic bunch graph matching, and linear discriminant analysis; while the two modalities are visible and thermal images. The performance of a face recognition system can be measured by accuracy (AC) rate and false accept rate (FAR). To enhance the performance of face recognition especially at a distance, score fusion techniques are applied, which combine several scores from multiple matchers and multiple modalities. Our experiments are conducted with the ASUMS face dataset consisting of two spectral images (visible and thermal) from 135 subjects. The experimental results show that the face recognition with small image scales (simulating long distances) have low performance (e.g., AC=91.36%, FAR=8.64% for 20×20-pixel images); and score fusion can greatly improve accuracy (99.34%) meanwhile reduce FAR (0.31%).

In this paper, we introduce an incremental version of recently proposed constrained Linear Discriminant Analysis (LDA). In addition of application in constrained LDA problems, our algorithm which we call Online Discriminative Component Analysis (ODCA) is usable in standard incremental LDA problems. ODCA incrementally computes the solution of LDA with the time complexity lower than most incremental algorithm for LDA while keeps the accuracy of final result as close as possible to offline version. This is done using a special formulation for the scatter matrix updating along with Eigen-space calculation. By exploiting such formulation, the proposed algorithm made capable of updating the solution where a data point added or removed from the problem. It is also usable in problems where its data points have concept drift property. To show efficiency of proposed algorithm, its speed is compared to other existing incremental algorithms as order of complexity. In addition, the classification accuracy of our approach is experimentally compared to other algorithms.

A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from resulting Gabor filtered images. kernel discriminant analysis (KDDA) is then applied to the feature vectors for dimension reduction as well as class separability enhancement. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of KDDA over other Kernel methods such as kernel principal component analysis (KPCA) and general discriminant analysis (GDA). Significant improvements are also observed when features are extracted from Gabor filtered images instead of the original images. A 94% accuracy has been observed for the novel Gabor + KDDA method on the FERET database using a simple classifier, which could be further improved by employing a more complex classifier and distance measurer.

Locality preserving partial least squares discriminant analysis for face recognition

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

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

Sparse regularization discriminant analysis for face recognition

Five cranial nonmetric traits for sex estimation for sex estimation are classified by score according to geometry. The population of origin is one of the factors influencing cranial nonmetric traits. Moreover, among the five cranial traits, the robust traits for estimating sex varied across population. The aim of this study is to suggest the most useful method for sex estimation and demonstrate the need of a suitable method for each population. One-hundred thirty-five three-dimensional skull images from 21st century Korean autopsy cadavers were evaluated using the ordinal scoring system of five cranial nonmetric traits as outlined in Buikstra & Ubelaker (1994). All scores of each trait were analyzed by linear discriminant and decision tree analyses for sex estimation. The frequency of each trait was analyzed and compared to populations from other studies. The accuracy for both sexes was 88.1% by discriminant analysis and 90.4% by decision tree. The traits with the highest accuracy were the glabella and mastoid process in both discriminant analysis and decision tree. Sex estimation in modern Korean cadavers using the cranial nonmetric method was shown to be highly accurate by both discriminant analysis and decision tree. When comparing the pattern of frequency scores in this study with those of other populations, the pattern of trait scores for estimating sex was different for each population, even among populations in the same Asian region, which suggests the need for methods suited for specific populations.

Face recognition has been of interest to a growing number of researchers due to its applications on security. There are numerous face recognition algorithms proposed by researchers. However, there is no unified framework for the integration. In this paper, we implement different existing well-known algorithms, eigenface, Fisher-face, elastic graph matching (EGM), support vector machine (SVM) and neural network, to give a comprehensive testing under same face databases. Moreover, we present a face recognition committee machine (FRCM), which is a novel approach for assembling the outputs of various face recognition algorithms to obtain a unified decision with improved accuracy. The machine consists of an ensemble of the above algorithms to cope with various face images. We have tested our system with ORL face database and Yale face database. A comparative experimental result of different algorithms with the committee machine demonstrates that the proposed system achieves improved accuracy over the individual algorithms.

Face recognition algorithms enable computational devices to automatically recognize human faces and have been adopted by many big tech companies. These algorithms have shown fairly acceptable performance in criminal identification, healthcare, advertisement, access and security, payments and other different areas. In this paper, we attempt comparatively to identify best algorithms in terms of accuracy when applied on datasets that have considerable facial expression and pose illumination challenges. We selected benchmark datasets and applied preprocessing techniques to suppress noise in the images. We then applied different algorithms for feature extraction and then feed these features to the classifier. Based on the conducted experiments, we observed that the Local Binary Pattern Histogram algorithm outperformed the other two selected algorithms by about 1% against Linear Discriminant Analysis (LDA) and about 9% against Principal Component Analysis (PCA). In addition, LDA outperformed the other two by obtaining about 99.532% of accuracy on JAFFE dataset.

Research was conducted to investigate the use of optical, air-born acoustic and plasma charge signals for detecting defects in laser welds. Full penetration, partial penetration and gapped-full penetration butt-joint welds were made in 1.4mm cold-rolled steel sheet. A 3 kW CO2 laser was used to produce these three classes of welds while laser power, travel speed and joint fitup were varied to produce the various conditions. Visible light, audible sound and electrical charge near the plume were measured simultaneously in real-time using a silicon photodiode, a condensor microphone and a plasma charge sensing gas nozzle, respectively. The spectra of the sensor signals were analyzed by a statistical technique known as Linear Discriminant Analysis (LDA). Stepwise LDA was used to identify the most important frequency bands and canonical discriminant analysis was employed to develop discriminant functions. Reliability analysis of the training samples revealed that this LDA method classified laser welds with reliability ranging from 85% to 96%, depending on the welds selected for inclusion in the training set. When the linear discriminant function was validated using new (untrained) samples, it distinguished the three different classes of laser welds with approximately 87% accuracy. Among the three signals, sound produced better separation between the classes of welds, although a discriminator based on all three signals combined and one that used only plasma charge and acoustic signals were of almost equal reliability.Research was conducted to investigate the use of optical, air-born acoustic and plasma charge signals for detecting defects in laser welds. Full penetration, partial penetration and gapped-full penetration butt-joint welds were made in 1.4mm cold-rolled steel sheet. A 3 kW CO2 laser was used to produce these three classes of welds while laser power, travel speed and joint fitup were varied to produce the various conditions. Visible light, audible sound and electrical charge near the plume were measured simultaneously in real-time using a silicon photodiode, a condensor microphone and a plasma charge sensing gas nozzle, respectively. The spectra of the sensor signals were analyzed by a statistical technique known as Linear Discriminant Analysis (LDA). Stepwise LDA was used to identify the most important frequency bands and canonical discriminant analysis was employed to develop discriminant functions. Reliability analysis of the training samples revealed that this LDA method classified laser welds with rel...

A feature extraction and fusion algorithm was constructed by combining principal component analysis (PCA) and linear discriminant analysis (LDA) to detect a fault state of the induction motor. After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment, the reference data were used to produce matching values. In a diagnostic step, two matching values that were obtained by PCA and LDA, respectively, were combined by probability model, and a faulted signal was finally diagnosed. As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief, it shows excellent performance under the noisy environment. The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.