Abstract
The problem of face detection has been one of the main topics in computer vision investigation and lots of methods have been proposed to solve it. One of the most important is the algorithm proposed by Viola and Jones that offer good results. Many studies have used this algorithm but none have analysed the advantages or disadvantages of using a certain type of feature in either the detection or the computation time. In this article we analyse the Viola algorithm [12] and other derivatives from the point of view of input characteristics and computing time.
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
Triggs, B., Dalal, N.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Schneiderman, H.: A statistical approach to 3d object detection applied to faces and cars. PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (2000)
Li, Y., Lao, S., Huang, C., Ai, H.: High-performance rotation invariant multiview face detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 671–686 (2007)
Levi, K., Weiss, Y.: Learning object detection from a small number of examples: the importance of good features (2004)
Zhang, Z., Li, S.: Floatboost learning and statistical face detection. IEEE Transactions Pattern Analysis and Machine Intelligence 26(9), 1112–1123 (2004)
Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.-Y.: Statistical learning of multi-view face detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 67–81. Springer, Heidelberg (2002)
Maydt, J., Lienhart, R.: An extended set of haar-like features for rapid object detection. In: IEEE ICIP, pp. 900–903 (2002)
Hori, O., Mita, T., Kaneko, T.: Joint haar-like features for face detection. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1619–1626 (2005)
Maenpaa, T., Ojala, T., Pietikainen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Zhang, J., Paisitkriangkrai, S., Shen, C.: Face detection with effective feature extraction. CoRR (2010)
Kanade, T., Rowley, H., Baluja, S.: Rotation invariant neural network-based face detection. Technical report, Computer Science Department, Pittsburgh, PA (1997)
Jones, M., Viola, P.: Robust real-time face detection. International Journal of Computer Vision 5, 137–154 (2004)
Snow, D., Viola, P., Jones, M.: Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision 63, 153–161 (2005)
Zhang, Z., Zhang, C.: A survey of recent advances in face detection. Technical report, Microsoft Research Microsoft Corporation (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Mena, A.P., Bachiller Mayoral, M., Díaz-Lópe, E. (2015). Comparative Study of the Features Used by Algorithms Based on Viola and Jones Face Detection Algorithm. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-18833-1_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18832-4
Online ISBN: 978-3-319-18833-1
eBook Packages: Computer ScienceComputer Science (R0)