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Advances in Intelligent Informatics pp 61–76Cite as

SV-M/D: Support Vector Machine-Singular Value Decomposition Based Face Recognition

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 320))

Abstract

This paper presents a novel method for Face Recognition (FR) by applying Support Vector Machine (SVM) in addressing this Computer Vision (CV) problem. The SVM is a capable learning classifier capable of training polynomials, neural networks and RBFs. Singular Value Decomposition (SVD) are used for feature extraction and while SVM for classification. The proposed algorithm is tested on four databases, viz., FERET, FRGC Ver. 2.0, CMU-PIE and Indian Face Database. The singular values are filtered, sampled and classified using Gaussian RBF kernal for SVM. The results are compared with other known methods to establish the advantage of SV-M/D. The recall rate for the proposed system is about 90%. The colossal augmentation is due to the simple but efficient feature extraction and the ability to learn in a high dimensional space.

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Author information

Authors and Affiliations

  1. Indian Institute of Science (IISc), C.V. Raman Road, Bangalore, 560 012, KA, India

    Mukundhan Srinivasan

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  1. Mukundhan Srinivasan
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Correspondence to Mukundhan Srinivasan .

Editor information

Editors and Affiliations

  1. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

  2. Indian Institute of Information Technology and Management- - Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  3. Faculty of Design, Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  4. Department of Information Systems and Operations Management, University of Florida, Warrington College of Business, Florida, Florida, USA

    Selwyn Piramuthu

  5. University of Applied Sciences, Institute for Information Systems, Olten, Switzerland

    Thomas Hanne

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Srinivasan, M. (2015). SV-M/D: Support Vector Machine-Singular Value Decomposition Based Face Recognition. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-11218-3_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11217-6

  • Online ISBN: 978-3-319-11218-3

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