Algorithm Symmetric 2-DLDA for Recognizing Handwritten Capital Letters

Statistical pattern recognition is the process of using statistical techniques to obtain information and make informed decisions based on data measurements. It is possible to solve the doubt inherent in the objective function of the 2-Dimension Linear Discriminant Analysis by employing the symmetrical 2-Dimension Linear Discriminant Analysis approach. Symmetrical 2-dimensional linear discriminant analysis has found widespread use as a method of introducing handwritten capital letters. Symmetric 2-DLDA, according to Symmetric 2-DLDA, produces better and more accurate results than Symmetric 2-DLDA. So far, pattern recognition has been based solely on computer knowledge, with no connection to statistical measurements, such as data variation and Euclidean distance, particularly in symmetrical images. As a result, the aim of this research is to create algorithms for recognizing capital letter patterns in a wide range of handwriting. The ADL2-D symmetric method is used in this study as the development of the ADL2-D method. The research results in an algorithm that considers the left and right sides of the image matrix, as opposed to ADL2-D, which does not consider the left and right sides of the image matrix. In pattern recognition, the results with symmetric ADL2-D are more accurat

BDPCA plus LDA : a novel fast feature extraction technique for face recognition

2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness

Face recognition has been an important issue in computer vision and pattern recognition over the last several decades (Zhao et al., 2003). While human can recognize faces easily, automated face recognition remains a great challenge in computer-based automated recognition research. One difficulty in face recognition is how to handle the variations in expression, pose an

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Linear discriminant analysis : a detailed tutorial

Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction problems as a preprocessing step for machine learning and pattern classification applications. At the same time, it is usually used as a black box, but (sometimes) not well understood. The aim of this paper is to build a solid intuition for what is LDA, and how LDA works, thus enabling readers of all levels be able to get a better understanding of the LDA and to know how to apply this technique in different applications. The paper first gave the basic definitions and steps of how LDA technique works supported with visual explanations of these steps. Moreover, the two methods of computing the LDA space, i.e. class-dependent and class-independent methods, were explained in details. Then, in a step-by-step approach, two numerical examples are demonstrated to show how the LDA space can be calculated in case of the class-dependent and class-independent methods. Furthermore, two of the most common LDA problems (i.e. Small Sample Size (SSS) and non-linearity problems) were highlighted and illustrated, and state-of-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted with different datasets to (1) investigate the effect of the eigenvectors that used in the LDA space on the robustness of the extracted feature for the classification accuracy, and (2) to show when the SSS problem occurs and how it can be addressed

Null Space Approach Of Fisher Discriminant Analysis For Face Recognition

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2006Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2006Günümüzde birçok farklı sistem, insanların kendi servislerine erişimlerinde kimlik onaylamak veya belirlemek için güvenilir kimlik tanıma projelerine ihtiyac duymaktadır. Bu tür projelerdeki asıl amaç sunulan servislere erişimlerin sadece yetkili kullanıcıya verilmesini garanti etmektir. Bu ve buna benzer birkaç uygulama binalara, bilgisayar sistemlerine, dizüstü bilgisayarlara, telefonlara, ATM’lere güvenli erişimlerdir. Biometrik tanıma veya sadece biometrik, insanların fiziksel ve davranışsal özelliklerinin otomatik tanınması anlamına gelmektedir. Bu çalışmada, yüz tanıma için Fisher Diskriminant Analizine sıfır uzay yaklaşımı gerçekleştirilmiştir. Yüz tanıma genel nesne tanıma problemlerinin bir alt alanıdır. Herhangi birini yüzünü baz alarak tanımak biometrik içerisinde yanıltılması güç bir yöntemdir. PCA ise görüntü işleme alanında boyut küçültmede sıkça kullanılan bir yöntemdir. Aynı zamanda Karhunen-Loeve olarak da bilenen bu metot, boyutları küçülten bir lineer izdüşüm seçerek tüm izdüşüm örnekleri arasındaki dağılımı en yüksek dereceye getirir. Sınıfiçi dağılım matrisinin sıfır uzayı küçük örnek boyutu probleminin en diskriminatif bilgisini göstermektedir. Diğer metotlar sıfır uzayını kaldırdığı halde, sıfır uzay tabanlı Lineer Diskriminant Analizi sıfır uzayının tüm avantajlarını kullanmaktadır. Bu yöntem performans için en uygun olduğunu kanıtlamaktadır. Sıfır Uzayı Lineer Diskriminant Analizi algoritması ve bunun için en uygun durum çalışmada gösterilmiştir. Yöntemimiz diğer bütün sıfır uzayı yaklaşımlarından daha basit, işlemsel maliyet ve performans açısından daha uygundur. Deneyler farklı yüz veritabanlarında, farklı yüz ifadeleri kullanılarak, farklı sınıf sayısı ve farklı özvektör sayısı baz alınarak gerçekleştirilmiş ve önerilen metotların etkinlikleri ölçülmüştür.A wide variety of systems require reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user, and not anyone else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones and ATMs. Biometric recognition, or simply biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. Face recognition from images is a sub-area of the general object recognition problem. Identifying an individual from his or her face is one of the most nonintrusive modalities in biometrics. It is of particular interest in a wide variety of applications. PCA is a techique commonly used in dimension reduction in computer vision and particularly in face recognition. PCA techniques, also known as Karhunen-Loeve methods, choose a linear projection that reduces the dimensionality while maximizing the scatter of all projected samples. The null space of the within-class scatter matrix is found to express most discriminative information for the small sample size problem (SSSP). The null space-based LDA takes full advantage of the null space while the other methods remove the null space. It proves to be optimal in performance. From the theoretical analysis, we present the NLDA algorithm and the most suitable situation for NLDA. Our method is simpler than all other null space approaches, it saves the computational cost and maintains the performance simultaneously. Experiments are carried out on different face data sets, different facial expression, different class count and different eigenvalue count to demonstrate the effectiveness of the proposed methods.Yüksek LisansM.Sc

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms

A New Palm Print Recognition Approach by Using PCA & Gabor Filter

The key problems that involve in identification of palm print are searching for the better match from the test sample taken from input and also the available templates in the palm print database. The selection of the features and measuring similarity are 2 basic to be resolved. A feature that has higher discriminating ability should need to show a large variation between samples taken from totally different persons and small variation between samples taken from the palm of same person. Principal lines with information points are consider as very helpful palm print features and are successfully used for the aim of verification. Excluding these features there are many various features present in a palm print like: wrinkle features, geometry features, minutiae features and delta point features. It�s noted that each one of those features of palm are involved with the native attributes supported points or line segments. 2 key points in palm print identification are: first is to develop an efficient algorithm that extracts helpful features and second is to correctly measure the similarity of 2 features sets. In contrast to the existing technique, propose a combine selection technique for identification by using the palm print feature base pattern matching by combining native and global palm print features in some stratified fashion. In this work, use PCA, Gabor Filter and KNN for the aim of classification and matching. This work show palm print authentication system operates in 2 ways in which first is enrolment and the second is verification. In enrolment, a user needs to offer palm print samples many times to the system. The samples is captured with the use of any image capturing device that then pre-processed and so extraction of features is done to provide the templates that keep template database. For verification user is instruct to produce his/her user ID and palm print sample, then the palm print sample are pre-processed and extraction of feature is done to compared it with templates keep within the database that belonging to constant user ID