Abstract
A novel local texture descriptor, called multi-block quad binary pattern (MB-QBP), is proposed in this paper. To demonstrate its effectiveness on local feature representation and potential usage in computer vision applications, the proposed MB-QBP is applied to face detection. Compared with the multi-block local binary pattern (MB-LBP), MB-QBP has more features to conduct a better training process to refine the classifier. Consequently, the over-fitting problem becomes much smaller in the MB-QBP-based classifier. Extensive simulation results conducted by using the test images from the BioID and CMU+MIT databases have clearly shown that the proposed MB-QBP-based face detector outperforms the MB-LBP-based approach by about 6 % on the correct detection rate under the same training conditions.
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References
Jun, B., Kim, D.: Robust face detection using local gradient patterns and evidence accumulation. Pattern Recogn. 45, 3304–3316 (2012)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19, 1635–1650 (2010)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, CVPR 2001, vol. 1, p. I-511. IEEE (2001)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29, 51–59 (1996)
Wu, B., Ai, H., Huang, C., Lao, S.: Fast rotation invariant multi-view face detection based on real adaboost. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004, Proceedings, pp. 79–84. IEEE (2004)
Zhang, L., Chu, R.F., Xiang, S., Liao, S.C., Li, S.Z.: Face detection based on multi-block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007)
Wei, Z., Dong, Y., Zhao, F., Bai, H.: Face detection based on multi-scale enhanced local texture feature sets. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 953–956. IEEE (2012)
Friedman, J., Hastie, T., Tibshirani, R., et al.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Statist. 28, 337–407 (2000)
Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments, pp. 07–49. University of Massachusetts, Amherst, Technical Report (2007)
Wu, J., Brubaker, S.C., Mullin, M.D., Rehg, J.M.: Fast asymmetric learning for cascade face detection. IEEE Trans. Pattern Anal. Mach. Intell. 30, 369–382 (2008)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under the Grants 61250009 and 61372107, in part by the Xiamen Key Science and Technology Project Foundation under the Grant 3502Z20133024, and in part by the High-Level Talent Project Foundation of Huaqiao University under the Grants 14BS201 and 14BS204.
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Ge, Z., Cai, C., Zeng, H., Zhu, J., Ma, KK. (2015). Face Detection Based on Multi-block Quad Binary Pattern. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_52
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DOI: https://doi.org/10.1007/978-3-319-16628-5_52
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