Independent analysis of feature based face recognition algorithms under varying poses

2014 ◽  
Vol 18 (2) ◽  
pp. 61-71
Author(s):  
K.R. Singh ◽  
M.A. Zaveri ◽  
M.M. Raghuwanshi
Keyword(s):  
Face Recognition ◽  
Recognition Algorithms ◽  
Independent Analysis ◽  
Feature Based

LINEAR AND NONLINEAR FEATURE-BASED FUSION ALGORITHMS FOR FACE RECOGNITION

2006 ◽  
Vol 04 (04) ◽  
pp. 659-676
Author(s):  
JIAN HUANG ◽  
PONGCHI YUEN ◽  
WEN-SHENG CHEN ◽  
JIANHUANG LAI ◽  
XINGE YOU
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Recognition Algorithms ◽  
Face Recognition System ◽  
Combination Scheme ◽  
Feature Based ◽  
Linear And Nonlinear ◽  
Nonlinear Feature ◽  
Weighted Combination

Integration of various face recognition algorithms has proved to be a feasible approach to improve the performance of a face recognition system. Different face recognition algorithms are often based on different representations of the input patterns or on extracted features and hence may complement each other. Linear and nonlinear feature based algorithms can capture and handle different kinds of variations, such as pose, illumination and expression variations. To make full use of the different advantages of different classifiers, we propose combining four linear and nonlinear face recognition algorithms via a weighted combination scheme to improve the recognition performance of a face recognition system. The FERET, YaleB and CMU PIE database are used for evaluating the combination scheme and the results confirm the effectiveness of the proposed combination scheme.

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

scholarly journals Illumination Normalization Algorithm Combination Analysis in Face Recognition

2017 ◽  
Vol 9 (3) ◽  
pp. 334-339
Author(s):  
Rokas Semėnas
Keyword(s):  
Face Recognition ◽  
Large Scale ◽  
Real Life ◽  
Direct Approach ◽  
Small Scale ◽  
Test Results ◽  
Illumination Normalization ◽  
Recognition Algorithms ◽  
Life Problems

Face recognition programs have many practical usages in various fields, such as security or entertainment. Existing recognition algorithms must deal with various real life problems – mainly with illumination. In practice, illumination normalization models are often used only for Small-scale futures extraction, ignoring Large-scale features. In this article, new and more direct approach to this problem is offered, used algorithms and test results are given.

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