Abstract
When a person passes by a surveillance camera a sequence of image is obtained. Before performing any analysis on the face of a person, the face first needs to be detected and secondary the quality of the different face images needs to be evaluated. In this paper we present a system based on four simple features including out-of-plan rotation, sharpness, brightness and resolution, to assess the face quality in a video sequence. These features are combined using both a local scoring system and weights. The system is evaluated on two databases and the results show a general agreement between the system output and quality assessment by a human.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Luo, H.: A Training-Based No-Reference Image Quality Assessment Algorithm. In: nternational Conference on image Processing (ICIP), pp. 2973–2976. IEEE Press, Los Alamitos (2004)
Subasic, M., Loncaric, S., Petkovic, T., Bogunvoic, H.: Face Image Validation System. In: 4th International Symposium on Image and Signal Processing and Analysis, pp. 30–36 (2005)
Fronthaler, H., Kollreider, K., Bigun, J.: Automatic Image Quality Assessment with Application in Biometrics. In: International Conference on Computer Vision and Pattern Recognition, IEEE Press, Los Alamitos (2006)
Xiufeng, G., Stan, Z., Rong, L., Zhang, P.: Standardization of Face Image Sample Quality. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 242–251. Springer, Heidelberg (2007)
Fourney, A., Laganiere, R.: Constructing Face Image Logs that are Both Complete and Concise. In: 4th Canadian Conference on Computer Vision and Robot Vision, IEEE Press, Canada (2007)
Nasrollahi, K., Rahmati, M., Moeslund, T.B.: A Neural Network Based Cascaded Classifier for Face Detection in Color Images with Complex Background. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 966–976. Springer, Heidelberg (2008)
Peng, K., Chen, L., Ruan, S., Kukharev, G.: A Robust Algorithm for Eye Detection on Gray Intensity Face Without Spectacles. Journals of Computer Science and Technology 5(3), 127–132 (2005)
Weber, F.: Some quality measures for face images and their relationship to recognition performance. In: Biometric Quality Workshop. National Institute of Standards and Technology, Maryland, USA, March 8-9 (2006)
Solina, F., Peer, P., Batagelj, B., Juvan, S., Kovac, J.: Color-based face detection in the 15 seconds of fame art installation. In: Conference on Computer Vision / Computer Graphics Collaboration for Model-based Imaging, Rendering, image Analysis and Graphical special Effects, France, pp. 38–47 (2003)
Hermes project (FP6 IST-027110), http://www.cvmt.dk/projects/Hermes/index.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nasrollahi, K., Moeslund, T.B. (2008). Face Quality Assessment System in Video Sequences. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds) Biometrics and Identity Management. BioID 2008. Lecture Notes in Computer Science, vol 5372. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89991-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-89991-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89990-7
Online ISBN: 978-3-540-89991-4
eBook Packages: Computer ScienceComputer Science (R0)