Independent analysis of feature based face recognition algorithms under varying poses

Author(s):  
K.R. Singh ◽  
M.A. Zaveri ◽  
M.M. Raghuwanshi
Author(s):  
JIAN HUANG ◽  
PONGCHI YUEN ◽  
WEN-SHENG CHEN ◽  
JIANHUANG LAI ◽  
XINGE YOU

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.


2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Shaokang Chen ◽  
Sandra Mau ◽  
Mehrtash T. Harandi ◽  
Conrad Sanderson ◽  
Abbas Bigdeli ◽  
...  

2017 ◽  
Vol 9 (3) ◽  
pp. 334-339
Author(s):  
Rokas Semėnas

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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