Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers

  • Geetika Singh
  • Indu Chhabra


Hybridization represents a promising approach for solving any recognition problem. This chapter presents two face recognition frameworks involving the hybridization of both the feature extraction and classification stages. Feature extraction is performed through the two proposed hybrid techniques, one based on the orthogonal combination of local binary patterns and histogram of oriented gradients, and the other based on gabor filters and Zernike moments. A hybrid metaheuristic classifier is also investigated for classification based on the integration of genetic algorithms (GA) and support vector machines (SVM), where GA has been used for the optimization of the SVM parameters. This is crucial since the optimal selection of SVM parameters ultimately governs its recognition accuracy. Experimental results and comparisons prove the suitability of the proposed frameworks as compared to the other baseline and previous works.


Face recognition Hybrid feature extraction Support vector machine Genetic algorithm GA-SVM classification 



The authors are grateful to the National Institute of Standards and Technology, AT&T Laboratories, and the Computer Vision Laboratory, Computer Science and Engineering Department, University of California, San Diego for providing the FERET, ORL, and Yale face databases respectively. During the course of this study, the first author was funded by an INSPIRE-Junior Research Fellowship (IF120810) from the Department of Science and Technology, Govt. of India.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Geetika Singh
    • 1
  • Indu Chhabra
    • 2
  1. 1.Department of Computer Science and ApplicationsMCM DAV College for WomenChandigarhIndia
  2. 2.Department of Computer Science and ApplicationsPanjab UniversityChandigarhIndia

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