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Feature Optimality-Based Semi-supervised Face Recognition Approach

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Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 515))

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Abstract

In this paper, a novel approach is proposed that cope with challenges such as illuminations, expressions, poses, and occlusions. The proposed methodology is a non-domination-based optimization technique with a semi-supervised classifier for recognizing a known and unknown face based on different scenarios. The classification is a robust method attaining aptness at different stages resulting in identification of proper training set with actual face image. Different datasets Yale Face Database, Extended Yale Face Database B, ORL database has been considered for our experiments. The performance of the proposed method has been evaluated on several grounds. Results show that the proposed method attains a better performance than the statistical methods.

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Authors and Affiliations

  1. Computer Engineering Department, Guru Nanak Dev University Regional Campus, Gurdaspur, India

    Taqdir

  2. Computer Engineering Department, National Institute of Technology (NIT), Jalandhar, India

    Renu dhir

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  1. Taqdir
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  2. Renu dhir
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Correspondence to Taqdir .

Editor information

Editors and Affiliations

  1. ANITS, Prof., Comp. Sci. & Engg. Dept. ANITS, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Dept. of ECE, Shri Ramswaroop Mem. Group of Prof. Clg Dept. of ECE, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. SCIS, University of Hyderabad , Hyderabad, India

    Siba K. Udgata

  4. KIIT University, School of Computer Engineering KIIT University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

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Taqdir, Renu dhir (2017). Feature Optimality-Based Semi-supervised Face Recognition Approach. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_6

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  • DOI: https://doi.org/10.1007/978-981-10-3153-3_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3152-6

  • Online ISBN: 978-981-10-3153-3

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