Abstract
This paper describes an analysis of the real-time system for face recognition from video monitoring images. First, we briefly describe main features of the standards for biometric face images. Available scientific databases have been checked for compliance with these biometric standards. Next, we concentrate on the analysis of the prepared face recognition application based on the eigenface approach. Finally, results of our face recognition experiments with images of reduced resolution are presented. It turned out that the proposed and tested algorithm is quite resistant to changing the resolution. The recognition results are acceptable even for low-resolution images (16×20 pixels).
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
Davis, M., Popov, S., Surlea, C.: Real-Time Face Recognition from Surveillance Video. In: Zhang, J., Shao, L., Zhang, L., Jones, G.A. (eds.) Intelligent Video Event Analysis and Understanding. SCI, vol. 332, pp. 155–194. Springer, Heidelberg (2011)
Marciniak, T., Drgas, S., Cetnarowicz, D.: Fast Face Localisation Using AdaBoost Algorithm and Identification with Matrix Decomposition Methods. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2011. CCIS, vol. 149, pp. 242–250. Springer, Heidelberg (2011)
Xu, Y., Jin, Z.: Down-sampling face images and low-resolution face recognition. In: The 3rd International Conference on Innovative Computing Information and Control, p. 392 (2008)
Zou, W.W., Yuen, P.C.: Very Low Resolution Face Recognition Problem. IEEE Transactions on Image Processing 21(1), 327–340 (2012)
Biometrics Technology Introduction, http://www.biometrics.gov/documents/biointro.pdf
ISO/IEC 19794-5:2005, Information technology – Biometric data interchange formats – Part 5: Face image data (2005)
ANSI/INCITS 385-2004, Information technology – Face Recognition Format for Data Interchange (2004)
Database of the Sheffield University, http://www.sheffield.ac.uk/eee/research/iel/research/face
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643–660 (2001)
Milborrow, S., Morkel, J., Nicolls, F.: The MUCT Landmarked Face Database. Pattern Recognition Association of South Africa 2010 (2010), http://www.milbo.org/muct/
Portion of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office; Phillips, P.J., et al.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)
The Face Annotation Interface, http://faint.sourceforge.net/
Frischholz, R. W.: The Face Detection Homepage, http://www.facedetection.com/
Kapur, J.P.: Face Detection in Color Images. EE499 Capstone Design Project, University of Washington Department of Electrical Engineering (1997), http://www.oocities.org/jaykapur/face.html
Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. BioID AG, Berlin, Germany (2001)
Nilsson, M.: Face Detection algorithm for Matlab. Blekinge Institute of Technology School of Engineering Department of Signal Processing, Ronneby, Sweden (2006), http://www.mathworks.com/matlabcentral/fileexchange/13701-face-detection-in-matlab
Rosa, L.: Face Recognition System 2.1 (2006), http://www.advancedsourcecode.com//face.asp
Agarwal, M., et al.: Face Recognition using Principle Component Analysis, Eigenface and Neural Network. In: Conference on Signal Sensors, Sensors, Visualization, Imaging, Simulation and Materials (2010)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection (1997)
Rzepecki, S.: Real-time localization and identification of faces in video sequences, (M.Sc. Thesis), Supervisor: Marciniak, T., Poznan University of Technology (2011)
Description of D-Link camera (2011), http://mydlink.dlink.com/products/DCS-930L , Data sheet, ftp://ftp10.dlink.com/pdfs/products/DCS-930L/DCS-930L_ds.pdf
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
Marciniak, T., Dabrowski, A., Chmielewska, A., Weychan, R. (2012). Face Recognition from Low Resolution Images. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2012. Communications in Computer and Information Science, vol 287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30721-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-30721-8_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30720-1
Online ISBN: 978-3-642-30721-8
eBook Packages: Computer ScienceComputer Science (R0)