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A Hierarchical FloatBoost and MLP Classifier for Mobile Phone Embedded Eye Location System

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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Abstract

This paper is focused on cellular phone embedded eye location system. The proposed eye detection system is based on a hierarchy cascade FloatBoost classifier combined with an MLP neural net post classifier. The system firstly locates the face and eye candidates’ areas in the whole image by a hierarchical FloatBoost classifier. Then geometrical and relative position information of eye-pair and the face are extracted. These features are input to a MLP neural net post classier to arrive at an eye/non-eye decision. Experimental results show that our cellular phone embedded eye detection system can accurately locate double eyes with less computational and memory cost. It runs at 400ms per image of size 256×256 pixels with high detection rates on a SANYO cellular phone with ARM926EJ-S processor that lacks floating-point hardware.

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References

  1. Riopka, T., Boult, T.: The Eyes Have It. In: Proceedings of ACM SIGMM Multimedia Biometrics Methods and Applications Workshop, Berkeley, CA, pp. 9–16 (2003)

    Google Scholar 

  2. Zhou, Z.-H., Geng, X.: Projection Functions for Eye Detection. Pattern Recognition 37(5), 1049–1056 (2004)

    Article  MATH  Google Scholar 

  3. Huang, W.J., Yin, B.C., Jiang, C.Y., Miao, J.: A New Approach for Eye Feature Extraction Using 3D Eye Template. In: ISIMP, pp. 340–343 (2001)

    Google Scholar 

  4. Huang, W.M., Mariani, R.: Face Detection and Precise Eyes Location. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 4, pp. 722–727 (2000)

    Google Scholar 

  5. Yong, M., Xiaoqing, D., Zhenger, W., Ning, W.: Robust Precise Eye Location under Probabilistic Framework. In: Proc. Sixth IEEE Int. Conf. Autom. Face Gest. Recogn., pp. 339–344 (2004)

    Google Scholar 

  6. Li, S.Z.: Floatboost Learning and Statistical Face Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(9), 1112–1123 (2004)

    Article  Google Scholar 

  7. Freund, Y., Schapire, R.: A Decision Theoretic Generalization of On-line Learning and an Application to Boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  8. Viola, P., Jones, M.: Robust Real-time Object Detection. Int. J. of Computer Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  9. Oliver, J., Klaus, J.K., Robert, W.F.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China

    Dan Chen

  2. Shenyang Institution of Automation, Chinese Academy of Sciences, Shenyang, China

    Dan Chen & Ning Xi

  3. School of Mechanical Engineering, Dalian University of Technology, Shenyang, China

    Xusheng Tang & Zongying Ou

Authors
  1. Dan Chen
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  2. Xusheng Tang
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  3. Zongying Ou
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  4. Ning Xi
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

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

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, D., Tang, X., Ou, Z., Xi, N. (2006). A Hierarchical FloatBoost and MLP Classifier for Mobile Phone Embedded Eye Location System. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_4

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  • DOI: https://doi.org/10.1007/11760023_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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