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Radial basis function neural network-based face recognition using firefly algorithm

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Abstract

This paper presents an adaptive technique for obtaining centers of the hidden layer neurons of radial basis function neural network (RBFNN) for face recognition. The proposed technique uses firefly algorithm to obtain natural sub-clusters of training face images formed due to variations in pose, illumination, expression and occlusion, etc. Movement of fireflies in a hyper-dimensional input space is controlled by tuning the parameter gamma (γ) of firefly algorithm which plays an important role in maintaining the trade-off between effective search space exploration, firefly convergence, overall computational time and the recognition accuracy. The proposed technique is novel as it combines the advantages of evolutionary firefly algorithm and RBFNN in adaptive evolution of number and centers of hidden neurons. The strength of the proposed technique lies in its fast convergence, improved face recognition performance, reduced feature selection overhead and algorithm stability. The proposed technique is validated using benchmark face databases, namely ORL, Yale, AR and LFW. The average face recognition accuracies achieved using proposed algorithm for the above face databases outperform some of the existing techniques in face recognition.

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Acknowledgements

This work is part of the Ph.D. research carried out at Birla Institute of Technology and Science, Pilani. We are grateful to the reviewers of this article for their constructive and valuable review comments which helped us to improve this article in many different ways.

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Authors and Affiliations

  1. Department of Computer Science and Information Systems, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, 333031, India

    Vandana Agarwal

  2. Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, 333031, India

    Surekha Bhanot

Authors
  1. Vandana Agarwal
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  2. Surekha Bhanot
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Correspondence to Vandana Agarwal.

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Conflict of interest

Birla Institute of Technology and Science, Pilani, is the employer for both the authors and has provided all necessary support to carry out the research work presented in this paper. There is no external funding received. The authors declare that they have no conflict of interest.

Informed consent

There is no direct involvement of the authors with human participants whose face images were used in the present study. The face databases ORL, Yale, AR and LFW are the benchmarked face databases used by the researchers all across the world. The permission to download AR face database was obtained from Prof. Aleix M. Martinez [44, 45]. The databases ORL, Yale and LFW are available free online [42, 43, 46, 47].

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Agarwal, V., Bhanot, S. Radial basis function neural network-based face recognition using firefly algorithm. Neural Comput & Applic 30, 2643–2660 (2018). https://doi.org/10.1007/s00521-017-2874-2

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  • DOI: https://doi.org/10.1007/s00521-017-2874-2

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