Abstract
In this paper, the framework and implementation of a real time multi-scale face detection system using appearance-based learning method and multi-pose hybrid learning approach. Multiple scale and pose based object detection is attractive since it could accumulate the face models by autonomous learning process. Face image, however, can be approximated even though it is represented with many scales. A real time face detection determines the location and size of each human face(if any) in an input image. Detecting varying human face in video frames is an important task in many computer vision applications such as human-computer interface. The face detection proposed in this paper employs hybrid learning approach and statistical method. We employ FuzzyART and RBF Network and Mahalanobis distance. We achieve a very encouraging experimental results.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Li, S.Z., Zhang, Z.: FloatBoost learning and statistical face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1112–1123 (2004)
Mohan, A., Papageorgiou, C., Poggio, T.: Example-Based Object Detection in Images by Components. IEEE Trans. on PAMI 23(4), 349–361 (2001)
Kasuba, T.: Simplefied Fuzzy ARTMAP. AI Expert, 18-25 (November 1993)
Erik Hjelmas, B.K.L.: Face Detection: A Survey. Computer Vision and Image Understanding 3(3), 236–274 (2001)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)
Cootes, T.F., Hill, A., Taylor, C.J., Haslam, J.: Use of active shape models for locating structure in medical images. Image and Vision Computing 12(6), 355–365 (1994)
Shakunaga, T.: An Object Pose Estimation System Using A Single Camera. In: Proc. of IEEE international conference, vol. 2, pp. 7–10 (1992)
Paulino, A., Araujo, H.: Pose Estimation for central catadioptric system: an analytic Approach. In: Proc. Of Pattern recognition, pp. 969–699 (2002)
Huang, J., Shao, X., Wechsler, H.: Face Pose discrimination using support vector machanism(SVM). In: Proc. of Pattern Recognition, vol. 1, pp. 154–156 (1998)
Hassoun, M.H.: Fundamentals of Artificial Neural Networks. MIT Press, Cambridge (1995)
Carpenter, G.A., et al.: Fuzzy ARTMAP: A neural network architecture for inceremetal supervised learning of analog multidimensional maps. IEEE Trans. Neural Networks 3(5), 698–712 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nam, MY., Rhee, PK. (2005). A Multi-scale and Multi-pose Face Detection System. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_30
Download citation
DOI: https://doi.org/10.1007/11552499_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
eBook Packages: Computer ScienceComputer Science (R0)