Textural feature based face recognition for single training images

2005 ◽  
Vol 41 (11) ◽  
pp. 640 ◽  
Author(s):  
R. Singh ◽  
M. Vatsa ◽  
A. Noore
Keyword(s):  
Face Recognition ◽  
Textural Feature ◽  
Training Images ◽  
Feature Based

scholarly journals Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Shaokang Chen ◽  
Sandra Mau ◽  
Mehrtash T. Harandi ◽  
Conrad Sanderson ◽  
Abbas Bigdeli ◽  
...  
Keyword(s):  
Face Recognition ◽  
Local Feature ◽  
Video Sequences ◽  
Still Images ◽  
Feature Based

Face Recognition Using DT-CWT Feature-Based 2DPCA

2010 ◽  
Author(s):  
Zhongxi Sun ◽  
Wankou Yang ◽  
Changyin Sun ◽  
Jifeng Shen
Keyword(s):  
Face Recognition ◽  
Feature Based

Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images

2020 ◽  
pp. 3-14
Author(s):  
Elisabeth Pfaehler ◽  
Liesbet Mesotten ◽  
Gem Kramer ◽  
Michiel Thomeer ◽  
Karolien Vanhove ◽  
...  
Keyword(s):  
Textural Feature ◽  
Segmentation Approach ◽  
Feature Based

scholarly journals Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

2018 ◽  
Vol 7 (4) ◽  
pp. 9 ◽  
Author(s):  
Shakir F. Kak ◽  
Firas M. Mustafa ◽  
Pedro R. Valente
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Training Image ◽  
Distance Measures ◽  
Manhattan Distance ◽  
Face Identification ◽  
Discrete Wavelet ◽  
Training Images ◽  
Matching Process ◽  
Cosine Distance

In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.

Face Recognition Technology

2011 ◽  
pp. 62-100
Author(s):  
Sanjay K. Singh ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
K.K. Shukla ◽  
Lokesh R. Boregowda
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Law Enforcement ◽  
Performance Comparison ◽  
Feature Analysis ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Wide Range ◽  
Biometric Systems ◽  
Feature Based

Face recognition technology is one of the most widely used problems in computer vision. It is widely used in applications related to security and human-computer interfaces. The two reasons for this are the wide range of commercial and law enforcement applications and the availability of feasible technologies. In this chapter the various biometric systems and the commonly used techniques of face recognition, Feature Based, eigenface based, Line Based Approach and Local Feature Analysis are explained along with the results. A performance comparison of these algorithms is also given.

A Lyapunov Theory-Based Neural Network Approach for Face Recognition

2010 ◽  
pp. 23-48 ◽  
Author(s):  
Li-Minn Ang ◽  
King Hann Lim ◽  
Kah Phooi Seng ◽  
Siew Wen Chin
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Principal Component ◽  
Lyapunov Stability Theory ◽  
Recognition System ◽  
Lyapunov Theory ◽  
Neural Network Approach ◽  
Linear Discriminant ◽  
Feature Based ◽  
Definition Of

This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems.

Sign in / Sign up

Export Citation Format

Share Document