Skip to main content
SpringerLink
Log in

An efficient multi-threshold AdaBoost approach to detecting faces in images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

During the past decade, the cascade AdaBoost approach based on Haar features has been one of the-state-of-art techniques for face detection, due to its good combination of accuracy and efficiency. Nevertheless recent study shows that more than 90 % of Haar features are non-discriminative under the single-threshold weak classifiers. In this paper, we present a new cascade AdaBoost face detection approach based on multi-threshold weak classifiers, called multi-threshold AdaBoost (MTAdaBoost). Compared with the existing multi-threshold approaches, the proposed approach is capable of intelligently choosing multiple thresholds, by computing the solutions of two optimal problems. Furthermore, its computational complexity is O(N), i.e. linear with respect to the number of examples, which is of the same scale as that of the single-threshold weak classifiers. This is made possible by exploiting the Kadane algorithm. The experimental results show that the usage of the proposed multi-threshold weak classifier leads to an effective face detector with much less features and at the same time maintaining the same or even better performance, when comparing to the conventional single-threshold weak classifier. In particular, less features give rise to simpler structure, which amounts to significant reduction of training time as well as faster detection speed. As a result, empirically our method processes an image with only half of the time consumed by the single-threshold weak classifiers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Amjad A, Griffiths A, Patwary MN (2012) Multiple face detection algorithm using colour skin modelling. IET Image Process 6(8):1093–1101

    Article  MathSciNet  Google Scholar 

  2. Bently J (1984) Programming pears: algorithm design techniques. Commun ACM 27(9):865–873

    Article  Google Scholar 

  3. Brubaker S, Brandt J, Sun J, Mullin M, Rehg J (2008) On the design of cascades of boosted ensembles for face detection. Int J Comput Vis IJCV 77(1–3):65–86

    Article  Google Scholar 

  4. Castrilln-Santana M, Dniz-Surez O, Hernndez-Sosa D, Lorenzo J (2011) A comparison of face and facial feature detectors based on the viola-jones general object detection framework. Mach Vis Appl 22(3):481–494

    Google Scholar 

  5. Cevikalp H, Triggs B, Franc V (2013) Face and landmark detection by using cascade of classifiers. In: Proceedings of the 10th IEEE international conference and workshops on automatic face and gesture recognition (FG), pp 1–7

  6. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139

    Article  MATH  MathSciNet  Google Scholar 

  7. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337–407

    Article  MATH  MathSciNet  Google Scholar 

  8. Huang C, Ai HZ, Li Y, Lao SH (2005) Vector boosting for rotation invariant multi-view face detection. In: The tenth IEEE international conference on computer vision ICCV, pp 446–453

  9. Huang C, Ai HZ, Li Y, Lao SH (2006) Learning sparse features in granular space for multi-view face detection. In: The 7th international conference on automatic face and gesture recognition, pp 401–406

  10. Jain V, Learned-Miller E (2011) Online domain adaptation of a pretrained cascade of classifiers. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 577–584

  11. Kawulok M, Szymanek J (2012) Precise multi-level face detector for advanced analysis of facial images. IET Image Process 6(2):95–103

    Article  MathSciNet  Google Scholar 

  12. Li S, Zhu L, Zhang Z, Blake A, Zhang H, Shum H (2002) Statistical learning of multi-view face detection. In: Proceedings of the 7th European conference on computer vision ECCV, pp 67–81

  13. Lienhar R, Kuranov A, Pisarevsky V (2003) Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Proceedings of the 25th DAGM symposium on pattern recognition, pp 297–304

  14. Liu C, Shum HY (2003) Kullback-leibler boosting. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 587–594

  15. Overett G, Petersson L (2007) Improved response modelling on weak classifiers for boosting. In: IEEE international conference on robotics and automation, pp 3799–3804

  16. Pham M-T, Cham T-J (2007) Online learning asymmetric boosted classifiers for object detection. In: IEEE conference on computer vision and pattern recognition, pp 1–8

  17. Rasolzadeh B, Petersson L, Pettersson N (2006) Response binning: improved weak classifiers for boosting. In: IEEE Intelligent vehicle symposium, pp 344–349

  18. Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2):197–227

    Google Scholar 

  19. Schapire RE (1999) A brief introduction to boosting. In: Proceedings of the 16th international joint conference on artificial intelligence

  20. Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336

    Article  MATH  Google Scholar 

  21. Tsao W-K, Lee AJT, Liu Y-H, Chang T-W, Lin H-H (2010) A data mining approach to face detection. Pattern Recogn 43(3):1039–1049

    Article  MATH  Google Scholar 

  22. Viola P, Jones M (2001) Fast and robust classification using asymmetric adaboost and a detector cascade. Adv Neural Inf Process Syst 2(3):1311–1318

    Google Scholar 

  23. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 511–518

  24. Wan C, Tian Y, Chen H, Wang X (2011) Rapid face detection algorithm of color images under complex background. In: Advances in neural networks, ISNN 2011. Lecture notes in computer science, vol 6676, pp 356–363

  25. Wu B, Ai HZ, Huang C, Lao SH (2004) Fast rotation invariant multi-view face detection based on real AdaBoost. In: Proceedings of the sixth IEEE international conference on automatic face and gesture recognition, pp 79–84

  26. Wu JX, Brubaker SC, Mullin MD, Rehg JM (2008) Fast asymmetric learning for cascade face detection. IEEE Trans Pattern Anal Mach Intell 30(3), 369–382

    Article  Google Scholar 

  27. Xiao R, Zhu H, Sun H, Tang X (2007) Dynamic cascades for face detection. In: IEEE 11th international conference on computer vision, pp 1–8

  28. Zhang L, Chu R, Xiang S, Liao S, Li S (2007) Face detection based on multi-block lbp representation. In: Proceedings of the international conference on advances in biometrics, pp 11–18

  29. Zhu X, Ramanan D (2012) Face detection, pose estimation, and landmark localization in the wild. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 2879–2886

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61232013, No. 61271434, No. 61175115. The authors appreciate the valuable suggestions of the reviewers.

Author information

Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Zhong Li, Weiqiang Wang, Xiaoqian Liu & Ke Lu

  2. CAS, Key Lab of Intell. Info. Process., Institute of Computing Technology, Beijing, China

    Weiqiang Wang

Authors
  1. Zhong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiqiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoqian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ke Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weiqiang Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, Z., Wang, W., Liu, X. et al. An efficient multi-threshold AdaBoost approach to detecting faces in images. Multimed Tools Appl 74, 885–901 (2015). https://doi.org/10.1007/s11042-013-1703-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1703-6

Keywords

Search

Navigation