Abstract
This work presents a face detection algorithm based on Multiscale Block Local Binary Patterns (MB-LBP) and an improved AdaBoost algorithm. The proposed boosting algorithm is capable of avoiding sample overfitting over its training process. This goal is achieved by making use of the information of sample misclassification frequency to update the weight distribution in the training process. Experimental results evidence some advantages of the proposed method over the classical AdaBoost algorithms, including the generalization capacity, overfitting avoidance and high precision rate on low-resolution images.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57, 137–154 (2001)
Li, G., Xu, Y., Wang, J.: An improved adaboost face detection algorithm based on optimizing skin color model. In: 2010 Sixth International Conference on Natural Computation (ICNC), vol. 4, pp. 2013–2015 (August 2010)
Hayashi, S., Hasegawa, O.: Detecting Faces from Low-Resolution Images. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006, Part I. LNCS, vol. 3851, pp. 787–796. Springer, Heidelberg (2006)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning Multi-scale Block Local Binary Patterns for Face Recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)
Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Rätsch, G., Onoda, T., Müller, K.-R.: Soft margins for adaboost. Mach. Learn. 42, 287–320 (2001)
Servedio, R.A.: Smooth boosting and learning with malicious noise. J. Mach. Learn. Res. 4, 633–648 (2003)
PICS: Psychological image collection at stirling (January 2012), http://pics.stir.ac.uk
UMIST: Face database (January 2012), http://www.sheffield.ac.uk/eee/research/iel/research/face
BioID: Face database (January 2012), https://www.bioid.com/download-center/software/bioid-face-database.html
FEI: Face database, http://fei.edu.br/~cet/facedatabase.html (January 2012)
Samaria, F.S., Samaria, F.S., Harter, A., Site, O.A.: Parameterisation of a stochastic model for human face identification (1994)
Rowley, H., Baluja, S., Kanade, T.: Rotation invariant neural network-based face detection. Technical Report CMU-CS-97-201, Computer Science Department, Pittsburgh, PA (December 1997)
Frischholz, R.: Bao face database at the face detection homepage (January 2012), http://www.facedetection.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Merjildo, D.A.F., Ling, L.L. (2012). A Robust AdaBoost-Based Algorithm for Low-Resolution Face Detection. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-32639-4_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
Online ISBN: 978-3-642-32639-4
eBook Packages: Computer ScienceComputer Science (R0)