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Neural Network Based Face Detection from Pre-scanned and Row-Column Decomposed Average Face Image

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4678))

Abstract

This paper introduces a methodology for detecting human faces with minimum constraints on the properties of the photograph and appearance of faces. The proposed method uses average face model to save the computation time required for training process. The average face is decomposed into row and column sub-matrices and then presented to the neural network. To reduce the time required for scanning the images at places where the probability of face is very low, a pre-scan algorithm is applied. The algorithm searches the faces in the image at different scales for detecting faces in different sizes. Arbitration between multiple scales and heuristics improves the accuracy of the algorithm. Experimental results are presented in this paper to illustrate the performance of the algorithm including accuracy and speed in detecting faces.

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Author information

Authors and Affiliations

  1. Ankara University, Faculty of Engineering, Electronics Engineering Department, 06100 Tandogan, Ankara, Turkey

    Ziya Telatar, Murat H. Sazlı & Irfan Muhammad

Authors
  1. Ziya Telatar
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  2. Murat H. Sazlı
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  3. Irfan Muhammad
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Editor information

Jacques Blanc-Talon Wilfried Philips Dan Popescu Paul Scheunders

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© 2007 Springer-Verlag Berlin Heidelberg

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Telatar, Z., Sazlı, M.H., Muhammad, I. (2007). Neural Network Based Face Detection from Pre-scanned and Row-Column Decomposed Average Face Image. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_27

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  • DOI: https://doi.org/10.1007/978-3-540-74607-2_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74606-5

  • Online ISBN: 978-3-540-74607-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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