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Neural Computing and Applications

, Volume 31, Issue 11, pp 7935–7944 | Cite as

Optimization of K-nearest neighbor using particle swarm optimization for face recognition

  • K. SasirekhaEmail author
  • K. Thangavel
Original Article

Abstract

Face biometric has received more attention to recognize a person in a right way. However, the face recognition is considered to be hard due to size, ethnicity, illumination, pose, various expression, and age. In this work, a novel approach is proposed to recognize the human face based on K-nearest neighbor (KNN) with particle swarm optimization (PSO). Initially, the features are extracted using local binary pattern. The metaheuristic optimization algorithms such as genetic algorithm, PSO, and ant colony optimization are investigated for feature selection. The KNN classifier is optimized using the population-based metaheuristic algorithm PSO. Finally, the face recognition is performed using the proposed PSO–KNN algorithm. In this research, experiments have been conducted on real-time face images collected from 155 subjects each with ten orientations using Logitech Webcam and also on ORL face dataset. The experimental result of the proposed PSO–KNN is compared with other benchmark recognition techniques such as decision table, support vector machine, multilayer perceptron and conventional KNN, to conclude the efficacy of the proposed approach.

Keywords

ACO Face biometric GA KNN LBP PSO 

Notes

Acknowledgements

Authors would like to thank UGC, New Delhi, for the financial support received under UGC Major Research Project No. 43-274/2014(SR).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Computer SciencePeriyar UniversitySalemIndia

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