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Positioning corners of human mouth based on local gradient operator

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Abstract

Face recognition has widespread applications in monitoring system, public security and home entertainment etc.. However, in practical application, there are many problems needed to be solved in face recognition technology. This paper presents a method to detect and locate accurately facial feature points based on local gradient operator. With Adaboost algorithm, we first detect roughly the mouth area in the face image, and then extract contours of mouth using local gradient operator. Finally, we use Ostu threshold to extract the binary contour around mouth corners according to the precise location of chain code tracing. Experimental results show that local gradient operator can detect and locate rapidly and accurately human mouth, and it is relatively robust against change of facial expressions as well as noise, which helps to improve face recognition rate.

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References

  1. Bichsel M (1991) Strategies of robust object recognition for the automatic identification of human faces [Dissertation], Zurich: ETH Zurich

  2. Brunelli R, Poggio T (1993) Face recognition: features versus templates. IEEE Trans Pattern Anal Mach Intell 15(10):1042–1052

    Article  Google Scholar 

  3. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685

    Article  Google Scholar 

  4. Cristinacce D, Cootes T (2007) Boosted regression active shape models. British Machine Vision Conference, London

    Book  MATH  Google Scholar 

  5. Dihua LI, Podolak LT, Lee SW (2002) Facial component extraction and face recognition with support vector machines. In Proceedings of 2th IEEE International Conference on Automatic Face and Gesture Recognition, Washington DC, USA. New York: IEEE Press, 76–81

  6. Feris RS, Gemmell J, Toyama K, Kruger V (2002) Hierarchical wavelet networks for facial feature localization. In: Proceedings of Automatic Face and Gesture Recognition, 118–123

  7. Fu HC, Lai PS, Lou RS, Pao H-T (2000) Face detection and eye localization by neural network based color segmentation. In: Proceedings of Neural Networks for Signal Processing. 2: 507–516

  8. Heisele B, Ho P, Poggio T (2001) Face recognition with support vector machines: global versus component-based approach. In: Proceedings of International Conference on Computer Vision. 2:688–694

  9. Hsu R-L, Abdel-Mottaleb M, Jain AK (2002) Face detection in color images. IEEE Trans Pattern Anal Mach Intell 24(5):696–706

    Article  Google Scholar 

  10. Kafai M, Le A, Bhanu B (2014) Reference face graph for face recognition. IEEE Trans Inf Forens Secur 9(12):2132–2143

    Article  Google Scholar 

  11. Kanade T (1973) Picture processing system by computer complex and recognition of human faces. Kyoto University, Department of Information Science

  12. Kin CY, Cipolla R (1996) A probabilistic framework for perceptual grouping of features for human face detection. In: Proceedings of Automatic Face and Gesture Recognition 16–21

  13. Krüger V, Sommer G (2002) Wavelet networks for face processing. J Opt Soc Am

  14. Kumar SA, Thyaghrajan KK (2013) Facial expression recognition with auto-illumination correction, In: Processing of 2013 International Conference on Green Computing, Communication and Conservation of Energy 1(5):843–846

  15. Marcel S (2006) http://www.idiap.ch/resources/faceverif/index.php, Databases of features for face verification

  16. Nguyen MH, Perez J, De La Torre F (2008) Facial feature detection with optimal pixel reduction SVM. In: Proceedings of 8th IEEE International Conference on Automatic Face and Gesture Recognition, New York: IEEE Press, 376–381

  17. Phung SL, Chai D, Bouzerdoum (2001) Skin color based face detection. In: Proceedings of Intelligent Information Systems Conference 171–176

  18. Rein H, Lien M, Jain AK (2002) Face detection in color images. IEEE Trans Pattern Anal Mach Intell 24(5):696–706

    Article  Google Scholar 

  19. Reinders MJT, Koch RWC, Gerbrands JJ (1996) Locating facial features in image sequences using neural networks. In: Proceedings of Automatic Face and Gesture Recognition, 230–235

  20. Reisfeld D, Wolfson H, Yeshurun Y (1995) Context-free attentional operators: the generalized symmetry transform. Int J Comput Vis 14:119–130

    Article  Google Scholar 

  21. Reisfeld D, Yeshurun Y (1992) Robust detection of facial features by generalized symmetry. In: Proceedings of International Conference on Pattern Recognition, 117–120

  22. Waite J, Vincent J (1992) A probabilistic framework for neural network facial feature location. Br Telecom Technol J 10(3):20–29

    Google Scholar 

  23. Wan KW, Lam KM, Ng KC (2005) An accurate active shape model for facial feature extraction. Pattern Recogn Lett 26(15):2409–2423

    Article  Google Scholar 

  24. Yan S, Liu C, Li SZ, Zhang H (2003) Face alignment using texture constrained active shape models. Image Vis Comput 21(1):69–75

    Article  Google Scholar 

  25. Yang G, Huang TS (1994) Human face detection in a complex background. Pattern Recogn 27:53–63

    Article  Google Scholar 

  26. Zhang LM, Lenders P (2000) Knowledge-based eye detection for human face recognition. In Proceedings of Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 1:117–120

  27. Zhang W, Li X, Yao P, Li B, Zhuang Z (2008) A robust eye localization algorithm for face recognition. J Electron 25(3):337–342

    Google Scholar 

Download references

Acknowledgments

This work is sponsored by Applied Basic Research Programs of Wuhan City (Grant No. 2014060101010029), and Natural Science Foundation of Hubei Province of China (Grant No. 2011CDB449).

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Authors and Affiliations

  1. International School of Software, Wuhan University, Wuhan, China

    Yulin Wang & Yixin Chen

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Wenjia Ding

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  1. Yulin Wang
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  2. Wenjia Ding
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  3. Yixin Chen
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Correspondence to Yixin Chen.

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Wang, Y., Ding, W. & Chen, Y. Positioning corners of human mouth based on local gradient operator. Multimed Tools Appl 75, 11815–11829 (2016). https://doi.org/10.1007/s11042-015-2627-0

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  • DOI: https://doi.org/10.1007/s11042-015-2627-0

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