Recognition using extended multiple discriminant analysis (EMDA) method

Feature extraction based on fuzzy 2DLDA

Based on multiple maximum scatter difference discrimination Dictionary learning, a novel face recognition algorithm is proposed. Dictionary used for sparse coding plays a key role in sparse representation classification. In this paper, a multiple maximum scatter difference discriminated criterion is used for dictionary learning. During the process of dictionary learning, the multiple maximum scatter difference computes its discriminated vectors from both the range of the between class scatter matrix and the null space of the within-class scatter matrix. The proposed algorithm is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the AR database and Extended Yale Database B in comparison with existing basic sparse representation and other classification methods, it shows that the performance is a little better than the original sparse representation methods with lower complexity.

Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional LDA some weaknesses. In this paper, we propose a new LDA-based method that can overcome the drawback existed in the traditional LDA method. It redefines the between-class scatter by adding a weight function according to the between-class distance, which helps to separate the classes as much as possible. At the same time, in this method, we firstly remove the null space of total scatter matrix which has been proved to be the common null space of both between-class and within-class scatter matrix, and useless for discrimination. Then in the lower-dimensional projected space, the null space of the resulting within-class scatter matrix is calculated. This lower-dimensional null space, combined with the previous projection, represents a subspace of the whole null space of within-class scatter matrix, and is really useful for discrimination. The optimal discriminant vectors of LDA are derived from it. Experiment results show our method achieves better performance in comparison with the traditional LDA methods.

The purpose of this empirical study conducted to determine the factors influencing student academic performance in Perbanas Institute Jakarta. Multiple Discriminant Analysis and Regression Logistic as main statistical multivariate analysis method were used in this study. Results of the analysis will be determined that the Logistic Regression method is more accurate and superior as well as determine the factors that influence students' academic performance Perbanas Institute. Student Academic Performance in the case study students of Perbanas Institute, using the comparison between discriminant analysis method and regression logistic were influenced by factors duration of study, student motivation, student family environmental factors, and Quality of Learning, and E-Learning System. This comparative study reveals that the logistic regression method is much better and more accurate than the Discriminant Analysis method.

Fault detection based on robust characteristic dimensionality reduction

This paper proposes a new method of face image feature extraction, namely, the fuzzy 2DLDA (F2DLDA) based on the 2D fisher discriminant criterion and fuzzy set theory. In the proposed method, we calculate membership degree matrix by FKNN, then we incorporate the membership degree into the definition of the between-class scatter matrix and within-class scatter matrix and get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. Experiments on the ORL and FERET face databases show that the new method can work well.

This paper presents a projection-based location system in an indoor wireless local area network (WLAN) environment. Our algorithm projects the received signal strength (RSS) onto a discriminative space such that the information of all access points (APs) is more efficiently utilized. The projection is determined by multiple discriminant analysis (MDA), thereby guaranteeing maximal discriminative information involved in the positioning system. The study conducts a series of experiments on the effects of our approach in a realistic indoor environment. The results show that not only is the positioning accuracy significantly improved, but the system cost, including the computation and data collection, is also greatly reduced at the same time. This is because our approach extracts only useful information for positioning, whereas the redundant noise is discarded to avoid the problem of overfitting and unnecessary calculations. Compared with prior works, this technique can produce a more graceful balance between the positioning accuracy and the computational complexity for the resource-weak clients.

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.

In this paper, an automatic face recognition system is proposed based on appearance-based features that focus on the entire face image rather than local facial features. The first step in face recognition system is face detection. Viola-Jones face detection method that capable of processing images extremely while achieving high detection rates is used. This method has the most impact in the 2000’s and known as the first object detection framework to provide relevant object detection that can run in real time. Feature extraction and dimension reduction method will be applied after face detection. Principal Component Analysis (PCA) method is widely used in pattern recognition. Linear Discriminant Analysis (LDA) method that used to overcome drawback the PCA has been successfully applied to face recognition. It is achieved by projecting the image onto the Eigenface space by PCA after that implementing pure LDA over it. Square Euclidean Distance (SED) is used. The distance between two images is a major concern in pattern recognition. The distance between the vectors of two images leads to image similarity. The proposed method is tested on three databases (MUCT, Face94, and Grimace). Different number of training and testing images are used to evaluate the system performance and it show that increasing the number of training images will increase the recognition rate.

Super-Resolution (SR) involves the registration of multiple images/frames and reconstruction of a single higher resolution image. The goal of this research is to use multip le, very-low resolution images, such as those produced from a video sequence in a wireless sensor network system, as input to the super-resolution process in a face recognition system. The algorithm used for face reco gnition is the Fisherfaces method with a n earest neighbor classifier used for the recognition decision. Super-resolution consists of two stages, a registration stage and a reconstruction stage. Testing images were segmented using a simple skin color detection approach. After cropping they were combined into groups of four to be used for the super-resolution algorithm using faces from three people. Each group of four images was used as an input to the Keren registration algorithm where the rotational and translation information was saved that was then entered into the robust super-resolution reconstruction algorithm to create a single high quality image, which was processed by the face recognition algorithm. An av erage of the same groups of four was tested as well as a centroid shifted average. Comparison was based on n earest neighbor classifier and on classification rates. The results were not in favor of the super-resolution method but instead, the centroid shifted average was the best in this study. Keywords: Face Recognition, Super-Resolution, Fish erfaces, Nearest Neighbor Classifier

Abstract. The aims of this study is to identify the variables used in financial distress prediction dominantly and to identify the best accuraction and clasification from the financial distress prediction models. The objects are 15 textile and garment companies listed in Indonesia Stock Exchange since 2010 to 2018 using logistic regression and multiple discriminant analysis methods. The variables used financial ratios indicators from the aspects of the operation capacity, liquidity, profitability, solvency, asset management capacity and growth capacity. The results of the study suggest that the model based on a logit function out performs the classification accuracy of the discriminant model. The classification power created by logistic regression was 85.92% while the classification power created by multiple discriminant analysis was 83.70%. The best accuracy came from logistic regression models with 71 from 71 observations predicted fall into health firm category were classified correctly (100%), and 19 from 64 textile and garment companies repondends predicted fall into financial distress category were classified correctly (70,30%). The most significant predictors of impeding firms failure appear to be debt to equity ratio, return on assets ratio, return on equity ratio and working capital to total assets. Keywords. Financial distress prediction; Financial ratio; Logistic regression, Multivariate discriminant analysis

Linear Discriminant Analysis (LDA) [1] is a well-known method for face recognition in feature extraction and dimension reduction. To solve the “small sample” effect of LDA, Two-Dimensional Linear Discriminant Analysis (2DLDA) [2] has been used for face recognition recently，but its could hardly take use of the relationship between the adjacent scatter matrix. In this paper, I improved the between-class scatter matrix, proposed paired-class scatter matrix for face representation and recognition. In this new method, a paired between-class scatter matrix distance metric is used to measure the distance between random paired between-class scatter matrix. To test this new method, ORL face database is used and the results show that the paired between-class scatter matrix based 2DLDA method （N2DLDA） outperforms the 2DLDA method and achieves higher classification accuracy than the 2DLDA algorithm.

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.

Face recognition is one of the most fundamental functions for many kinds of intelligent robots. Among many methods proposed for face recognition, linear approaches such as Eigenface or principal component analysis (PCA), Fisher’s linear discriminant (FLD), Fisherface, and common vector (CV), have attracted great attention because of their simplicity. It is known that when the number of training examples is large enough, we should use FLD; otherwise, we should use CV. This paper proposes a rough common vector (RCV) approach. The basic idea of RCV is to divide the feature space into two subspaces. One is spanned by the eigenvectors corresponding to the largest eigenvalues of the within-class scatter matrix, and another is spanned by the eigenvectors corresponding to the smallest eigenvalues. The later plays the role of the null space of the within-class scatter matrix, and is important for extracting useful discriminative features for recognition. RCV can be used regardless the within-class scatter matrix is singular or not. Experimental results on four databases show that RCV outperforms nearest neighbor classifier, Eigenface, Fisherface, and CV in most cases.

Face recognition based on multi-class mapping of Fisher scores