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Intelligent Engineering Informatics pp 29–37Cite as

SVM Based Method for Identification and Recognition of Faces by Using Feature Distances

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 695))

Abstract

In this paper, a scheme was presented to identify the locations of key features of a human face such as eyes, nose, chin known as the fiducial points and form a face graph. The relative distances between these features are calculated. These distance measures are considered to be unique identifying attributes of a person. The distance measures are used to train a Support Vector Machine (SVM). The identification takes place by matching the features of the presented person with the features that were used to train the SVM. The closest match results in identification. The Minimum Distance Classifier has been used to recognize a person uniquely using this SVM.

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References

  1. Tie, Y., Guan, L.: Automatic landmark point detection and tracking for human facial expressions. J. Image Video Proc. 2013, 8 (2013). https://doi.org/10.1186/1687-5281-2013-8

    Article  Google Scholar 

  2. Yun, T., Guan, L.: Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques. Pattern Recogn. 42(9), 1859–1868 (2009)

    Article  Google Scholar 

  3. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. J. Comput. Surv. (CSUR) 35(4), 399–458 (2003). https://doi.org/10.1145/954339.954342

    Article  Google Scholar 

  4. Nair, P., Cavallaro, A.: 3-D face detection, landmark localization, and registration using a point distribution model. IEEE Trans. Multimed. 11(4), 611–623 (2009). https://doi.org/10.1109/tmm.2009.2017629

    Article  Google Scholar 

  5. Shi, J., Samal, A., Marrx, D.: How effective are landmarks and their geometry for face recognition? Comput. Vis. Image Underst. Elseivier 102(2), 117–133 (2006)

    Article  Google Scholar 

  6. Cao, Z., Yin, Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. Comput. Vis. Pattern Recogn. (CVPR), IEEE. (2010) https://doi.org/10.1109/cvpr.2010.5539992

  7. Celikutan, O., Ulukaya, S., Sankur, B.: A comparative study of face landmarking techniques. EURASIP J. Image Video Process. (2013). Springer. https://doi.org/10.1186/1687-5281-2013-13

  8. O’Shaughnessy, D.: Automatic speech recognition: history, methods and challenges. Pattern Recogn. 41(10), 2965–2979 (2008). Elseiveier

    Article  Google Scholar 

  9. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. 3rd. ed. Pearson Education (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, St. Xavier’s College, Kolkata, India

    Jayati Ghosh Dastidar, Priyanka Basak, Siuli Hota & Ambreen Athar

Authors
  1. Jayati Ghosh Dastidar
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  2. Priyanka Basak
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  3. Siuli Hota
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  4. Ambreen Athar
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Corresponding author

Correspondence to Jayati Ghosh Dastidar .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, SRMGPC, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. Departamento de Computación, CINVESTAV-IPN, Mexico City, Mexico

    Carlos A. Coello Coello

  3. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

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

    Prasant Kumar Pattnaik

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© 2018 Springer Nature Singapore Pte Ltd.

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Cite this paper

Dastidar, J.G., Basak, P., Hota, S., Athar, A. (2018). SVM Based Method for Identification and Recognition of Faces by Using Feature Distances. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_4

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  • DOI: https://doi.org/10.1007/978-981-10-7566-7_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7565-0

  • Online ISBN: 978-981-10-7566-7

  • eBook Packages: EngineeringEngineering (R0)

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