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A new approach for face detection using the maximum function of probability density functions

Author

Listed:
  • Ha Che-Ngoc

    (Ton Duc Thang University)

  • Thao Nguyen-Trang

    (University of Science
    Vietnam National University)

  • Tran Nguyen-Bao

    (Soongsil University)

  • Trung Nguyen-Thoi

    (Ton Duc Thang University
    Ton Duc Thang University)

  • Tai Vo-Van

    (Can Tho University)

Abstract

This article establishes some theoretical results about the maximum function of probability density functions ( $$f_{\max }$$ f max ) and the integration of $$f_{\max }$$ f max ( $$If_{\max }$$ I f max ). Using the probability density function extracted from the image as a relatively stable feature of the image and $$If_{\max }$$ I f max as a measure the similarity between a “face” candidate region and a group of training face images, we propose a new face detection method, one of the most challenging tasks related to image analysis. The experiments demonstrate the competitiveness of the proposed method, especially in the case of rotated images. It also shows potential in real application of the researched problem.

Suggested Citation

  • Ha Che-Ngoc & Thao Nguyen-Trang & Tran Nguyen-Bao & Trung Nguyen-Thoi & Tai Vo-Van, 2022. "A new approach for face detection using the maximum function of probability density functions," Annals of Operations Research, Springer, vol. 312(1), pages 99-119, May.
    • Handle: RePEc:spr:annopr:v:312:y:2022:i:1:d:10.1007_s10479-020-03823-1
    DOI: 10.1007/s10479-020-03823-1
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    References listed on IDEAS

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    1. Michael Ng & Wilson Kwan, 2001. "High-Resolution Color Image Reconstruction with Neumann Boundary Conditions," Annals of Operations Research, Springer, vol. 103(1), pages 99-113, March.
    2. Calvin Johnson & Delia McGarry & John Cook & Nallathamby Devasahayam & James Mitchell & Sankaran Subramanian & Murali Krishna, 2003. "Maximum Entropy Reconstruction Methods in Electron Paramagnetic Resonance Imaging," Annals of Operations Research, Springer, vol. 119(1), pages 101-118, March.
    3. Shang-Ming Zhou & John Gan & Lida Xu & Robert John, 2009. "Fuzziness index driven fuzzy relaxation algorithm and applications to image processing," Annals of Operations Research, Springer, vol. 168(1), pages 119-131, April.
    4. Frank Pfeuffer & Michael Stiglmayr & Kathrin Klamroth, 2012. "Discrete and geometric Branch and Bound algorithms for medical image registration," Annals of Operations Research, Springer, vol. 196(1), pages 737-765, July.
    5. Li Wang & Ji Zhu, 2010. "Image denoising via solution paths," Annals of Operations Research, Springer, vol. 174(1), pages 3-17, February.
    6. Tai Vo Van & T. Pham-Gia, 2010. "Clustering probability distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1891-1910.
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    Citations

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    Cited by:

    1. Hieu Huynh-Van & Tuan Le-Hoang & Tai Vo-Van, 2024. "Classifying for images based on the extracted probability density function and the quasi Bayesian method," Computational Statistics, Springer, vol. 39(5), pages 2677-2701, July.

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