Assembled matrix distance metric for 2DPCA-based face and palmprint recognition

Author name used in this publication: David ZhangBiometrics Research Centre, Department of ComputingVersion of RecordPublishe

Bidirectional PCA with assembled matrix distance metric for image recognition

2006-2007 > 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

Time And Space Efficient Techniques For Facial Recognition

In recent years, there has been an increasing interest in face recognition. As a result, many new facial recognition techniques have been introduced. Recent developments in the field of face recognition have led to an increase in the number of available face recognition commercial products. However, Face recognition techniques are currently constrained by three main factors: recognition accuracy, computational complexity, and storage requirements. The problem is that most of the current face recognition techniques succeed in improving one or two of these factors at the expense of the others. In this dissertation, four novel face recognition techniques that improve the storage and computational requirements of face recognition systems are presented and analyzed. Three of the four novel face recognition techniques to be introduced, namely, Quantized/truncated Transform Domain (QTD), Frequency Domain Thresholding and Quantization (FD-TQ), and Normalized Transform Domain (NTD). All the three techniques utilize the Two-dimensional Discrete Cosine Transform (DCT-II), which reduces the dimensionality of facial feature images, thereby reducing the computational complexity. The fourth novel face recognition technique is introduced, namely, the Normalized Histogram Intensity (NHI). It is based on utilizing the pixel intensity histogram of poses\u27 subimages, which reduces the computational complexity and the needed storage requirements. Various simulation experiments using MATLAB were conducted to test the proposed methods. For the purpose of benchmarking the performance of the proposed methods, the simulation experiments were performed using current state-of-the-art face recognition techniques, namely, Two Dimensional Principal Component Analysis (2DPCA), Two-Directional Two-Dimensional Principal Component Analysis ((2D)^2PCA), and Transform Domain Two Dimensional Principal Component Analysis (TD2DPCA). The experiments were applied to the ORL, Yale, and FERET databases. The experimental results for the proposed techniques confirm that the use of any of the four novel techniques examined in this study results in a significant reduction in computational complexity and storage requirements compared to the state-of-the-art techniques without sacrificing the recognition accuracy

Palmprint Recognition Using Different Level of Information Fusion

The aim of this paper is to investigate a fusion approach suitable for palmprint recognition. Several number of fusion stageis analyse such as feature, matching and decision level. Fusion at feature level is able to increase discrimination power in the feature space by producing high dimensional fuse feature vector. Fusion at matching score level utilizes the matching output from different classifier to form a single value for decision process. Fusion at decision level on the other hand utilizes minimal information from a different matching process and the integration at this stage is less complex compare to other approach. The analysis shows integration at feature level produce the best recognition rates compare to the other method

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction

Applications of topological data analysis to natural language processing and computer vision

2022 Spring.Includes bibliographical references.Topological Data Analysis (TDA) uses ideas from topology to study the "shape" of data. It provides a set of tools to extract features, such as holes, voids, and connected components, from complex high-dimensional data. This thesis presents an introductory exposition of the mathematics underlying the two main tools of TDA: Persistent Homology and the MAPPER algorithm. Persistent Homology detects topological features that persist over a range of resolutions, capturing both local and global geometric information. The MAPPER algorithm is a visualization tool that provides a type of dimensional reduction that preserves topological properties of the data by projecting them onto lower dimensional simplicial complexes. Furthermore, this thesis explores recent applications of these tools to natural language processing and computer vision. These applications are divided into two main approaches: In the first approach, TDA is used to extract features from data that is then used as input for a variety of machine learning tasks, like image classification or visualizing the semantic structure of text documents. The second approach, applies the tools of TDA to the machine learning algorithms themselves. For example, using MAPPER to study how structure emerges in the weights of a trained neural network. Finally, the results of several experiments are presented. These include using Persistent Homology for image classification, and using MAPPER to visual the global structure of these data sets. Most notably, the MAPPER algorithm is used to visualize vector representations of contextualized word embeddings as they move through the encoding layers of the BERT-base transformer model

Enhancing the image quality of digital breast tomosynthesis

A novel imaging technology, digital breast tomosynthesis (DBT), is a technique that overcomes the tissue superposition limitation of conventional mammography by acquiring a limited number of X-ray projections from a narrow angular range, and combining these projections to reconstruct a pseudo-3D image. The emergence of DBT as a potential replacement or adjunct to mammographic screening mandates that solutions be found to two of its major limitations, namely X-ray scatter and mono-energetic reconstruction methods. A multi-faceted software-based approach to enhance the image quality of DBT imaging has the potential to increase the sensitivity and specificity of breast cancer detection and diagnosis. A scatter correction (SC) algorithm and a spectral reconstruction (SR) algorithm are both ready for implementation and clinical evaluation in a DBT system and exhibit the potential to improve image quality. A principal component analysis (PCA) based model of breast shape and a PCA model of X-ray scatter optimize the SC algorithm for the clinical realm. In addition, a comprehensive dosimetric characterization of a FDA approved DBT system has also been performed, and the feasibility of a new dual-spectrum, single-acquisition DBT imaging technique has also been evaluated.Ph.D

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