Abstract
In this paper a general framework for face detection is presented which taken into accounts both color and gray level images. For color images, skin color segmentation is used as the first stage to reduce search space into a few gray level regions possibly containing faces. And then in general for gray level images, techniques of template matching based on average face for searching face candidates and neural network classification for face verification are integrated for face detection. A qualitative 3D model of skin color in HSI space is used for skin color segmentation. Two types of templates: eyes-in-whole and face itself, are used one by one in template matching for searching face candidates. Two three-layer-perceptrons (MLPs) are used independently in the template matching procedure to verify each face candidate to produce two list of detected faces which are arbitrated to exclude most of the false alarms. Experiment results demonstrate the feasibility of this approach.
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
Y. Dai and Y. Nakano, Face-Texture Model Based on SGLD and its Application in Face Detection in a COLOR Scene, Pattern Recognition, Vol.29, No.6, pp1007–1016, 1996.
G. Z. Yang and T. S. Huang, Human Face Detection in a Complex Background, Pattern Recognition, Vol.27, No.1, pp53–63, 1994.
B. Moghaddam and A. Pentland, Probabilistic Visual Learning for Object Representation, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.19, No.7, pp.696–710, 1997.
B. Moghaddam and A. Pentland, Beyond Linear Eigenspaces: Bayesian Matching for Face Recognition, pp.230–243, in Face Recognition from Theory to Applications, edited by H. Wechsler et al., Springer 1998.
K. Sung and T. Poggio, Example-Based Learning for View-Based Human Face Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 20 No.1, pp39–51, 1998.
H. A. Rowley, S. Baluja and T. Kanade, Neural Network-Based Face Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 20 No. 1, pp23–38, 1998.
E. Osuna, R. Freund and F. Girosi, Training Support Vector Machines: an application to face detection, in Proc. of CVPR, Puerto Rico, 1997.
H. Schneiderman and T. Kanade, Probabilistic Modeling of Local Appearance and Spatial Relationships for Object, in Proc. CVPR, 1998.
C. Garcia, G. Tziritas, Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis, IEEE Trans. Multimedia, 1999, 1(3): 264~277
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Haizhou, A., Luhong, L., Guangyou, X. (2000). A General Framework for Face Detection. In: Tan, T., Shi, Y., Gao, W. (eds) Advances in Multimodal Interfaces — ICMI 2000. ICMI 2000. Lecture Notes in Computer Science, vol 1948. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40063-X_16
Download citation
DOI: https://doi.org/10.1007/3-540-40063-X_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41180-2
Online ISBN: 978-3-540-40063-9
eBook Packages: Springer Book Archive