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Face Recognition Using 2DPCA and ANFIS Classifier

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 336)

Abstract

With the growth of information technology coupled with the need for high security, the application of biometric as identification and recognition process has received special attention. The biometric authentication systems are gaining importance, and in particular, face biometric is more preferred for person authentication because of its easy and non-intrusive method during acquisition procedure. Face recognition is considered to be one of the most reliable biometric, when security issues are taken into concern. Various methods are used for face recognition. To recognize the face, feature extraction becomes a critical problem. In this paper, two-dimensional principle component analysis (2D-PCA) has been applied for feature extraction. The feature vectors are then applied to adaptive neuro-fuzzy inference system (ANFIS) classifier. The result indicates that ANFIS classifier yields 97.1 % of classification accuracy.

Keywords

  • Face recognition
  • 2D-PCA
  • Feature extraction
  • ANFIS

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Correspondence to Hitesh Shah .

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Shah, H., Kher, R., Patel, K. (2015). Face Recognition Using 2DPCA and ANFIS Classifier. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2220-0_1

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  • DOI: https://doi.org/10.1007/978-81-322-2220-0_1

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  • Publisher Name: Springer, New Delhi

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