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Efficient Small Face Detection in Surveillance Images Using Major Color Component and LDA Scheme

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Computational Intelligence and Security (CIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3802))

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Abstract

Since the surveillance cameras are usually covering wide area, human faces appear quite small. Therefore, their features are not identifiable and their skin color varies easily even under the stationary lighting conditions so that the face region cannot be detected. To emphasize the detection of small faces in the surveillance systems, this paper proposes the three stage algorithm: a head region is estimated by the DCFR(Detection of Candidate for Face Regions) scheme in the first stage, the face region is searched inside the head region using the MCC(major color component) in the second stage and its faceness is tested by the LDA(Linear Discriminant Analysis) scheme in the third stage. The MCC scheme detects the face region using the features of a face region with reference to the brightness and the lighting environment and the LDA scheme considers the statistical features of a global face region. The experimental results have shown that the proposed algorithm shows the performance superior to the other methods’ in detection of small faces.

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References

  1. Yang, G., Huang, T.S.: Human Face Detection in Complex Background. Pattern Recognition 27(1), 53–63 (1994)

    Article  Google Scholar 

  2. Dai, Y., Nakano, Y.: Face-texture model based on SGLD and its application in face detection in a color scene. Pattern Recognition 29, 1007–1017 (1996)

    Article  Google Scholar 

  3. Chai, D., Bouzerdoum, A.: A Bayesian approach to skin color classification in Ycbcr color space. In: IEEE region Ten Conference, vol. 2, pp. 421–424. Kuala Lampur, Malaysia (2000)

    Google Scholar 

  4. Lanitis, A., Taylor, C.J., Cootes, T.F.: An automatic face identification System Using Flexible Appearance Models. Image and Vision Computing 13(5), 393–401 (1995)

    Article  Google Scholar 

  5. Vaillant, R., Monrocq, C., Le Cun, Y.: An original approach for the localization of objects in images. IEEE Proc. Vision. Image and Signals Processing 141, 245–250 (1994)

    Article  Google Scholar 

  6. Kim, S., Lim, S., Lee, B., Cha, H., Hahn, H.: Block Based Face Detection Scheme Using Face Color and Motion Information. In: Proceedings of SPIE. Real-Time Imaging VIII, January 2004, vol. 5297, pp. 78–88 (2004)

    Google Scholar 

  7. Liao, P.-S., Chen, T.-S., Chung, P.-C.: A Fast Algorithm for Multilevel Thresholding. Information Science and Engineering 17, 713–727 (2001)

    Google Scholar 

  8. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, Soongsil University, Dongjak-ku, Seoul, Korea

    Kyunghwan Baek, Heejun Jang, Youngjun Han & Hernsoo Hahn

Authors
  1. Kyunghwan Baek
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  2. Heejun Jang
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  3. Youngjun Han
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  4. Hernsoo Hahn
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Editor information

Editors and Affiliations

  1. Microelectronic Instiute, Xidian University, 710071, Xi’an, China

    Yue Hao

  2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  3. School of Computer Science and Technology, Xidian University, Xi’an, China

    Yu-Ping Wang

  4. Department of Computer Science, Hong Kong Baptist University, Hong Kong,  

    Yiu-ming Cheung

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  6. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  7. Key Laboratory of Computer Networks and Information Security(Ministry of Education), Xidian University, 710071, Xi’an, China

    Jianfeng Ma

  8. National Laboratory of Antennas and Microwave Technology, Xidian University, 710071, Xi’an, Shanxi, P.R. China

    Yong-Chang Jiao

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© 2005 Springer-Verlag Berlin Heidelberg

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Baek, K., Jang, H., Han, Y., Hahn, H. (2005). Efficient Small Face Detection in Surveillance Images Using Major Color Component and LDA Scheme. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596981_42

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  • DOI: https://doi.org/10.1007/11596981_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30819-5

  • Online ISBN: 978-3-540-31598-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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