A Robust Face Recognition Algorithm Using Markov Stationary Features and Adjacent Pixel Intensity Difference Quantization Histogram

2011 ◽  
Author(s):  
Feifei Lee ◽  
Koji Kotani ◽  
Qiu Chen ◽  
Tadahiro Ohmi
Keyword(s):  
Face Recognition ◽  
Recognition Algorithm ◽  
Intensity Difference ◽  
Pixel Intensity ◽  
Adjacent Pixel ◽  
Robust Face Recognition

scholarly journals A New Evolutionary-Incremental Framework for Feature Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Mohamad-Hoseyn Sigari ◽  
Muhammad-Reza Pourshahabi ◽  
Hamid-Reza Pourreza
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Algorithm Design ◽  
Feature Selection Method ◽  
Recognition Algorithm ◽  
Comparison Result ◽  
Open Problems ◽  
Invasive Weed ◽  
Np Hard Problem ◽  
Robust Face Recognition

Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection. The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length. Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.

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