Abstract
This paper proposes fast and robust face recognition system for incremental data, which come continuously into the system. Fast and robust mean that the face recognition performs rapidly both of training and querying process and steadily recognize face images, which have large lighting variations. The fast training and querying can be performed by implementing compact face features as dimensional reduction of face image and predictive LDA (PDLDA) as face classifier. The PDLDA performs rapidly the features cluster process because the PDLDA does not require to recalculate the between class scatter, S b , when a new class data is registered into the training data set. In order to get the robust face recognition achievement, we develop the lighting compensation, which works based on neighbor analysis and is integrated to the PDLDA based face recognition.
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
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Etemad, K., Chellappa, R.: Discriminant analysis for Recognition of Human Face Images. J. opt. Soc. Am. A 14(8), 1724–1733 (1997)
Chen, W., Meng, J.-E., Wu, S.: PCA and LDA in DCT Domain. Pattern Recognition Letter (26), 2474–2482 (2005)
Yu, H., Yang, J.: A Direct LDA algorithm for High-Dimensional Data-with Applicaton to Face Recognition. Pattern Recognition 34, 2067–2070 (2001)
Noushath, S., Kumar, G.H., Shivakumara, P.: (2D)2 LDA : An Efficient Approach for Face Recognition. Pattern Recognition 39, 1396–1400 (2006)
Pang, S., Ozawa, S., Kasabov, N.: Incremental Linear Discriminant Analysis for Classification of Data Streams. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 35(5), 905–914 (2005)
Zhao, H., Yuen, P.C.: Incremental Linear Discriminant Analysis for Face Recognition. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 38(1), 210–221 (2008)
Ruiz-del-Solar, J., Quinteros, J.: Illumination compensation and normalization in eigenspace-based face recognition: A comparative study of different pre-processing approaches. Pattern Recognition Letter 29(14), 1966–1979 (2008)
Kurita, S., Tomikawa, T.: Study On Robust Pre-Processing For Face Recognition Under Illumination Variations. In: The Workshop of Image Electronics and Visual Computing 2010, Nice France (March 2010), (CDROM)
IGPS, Wijaya, Uchimura, K., Hu, Z.: Improving the PDLDA Based Face Recognition Using Lighting Compensation. In: The Workshop of Image Electronics and Visual Computing 2010, Nice France (March 2010), (CDROM)
IGPS, Wijaya, Uchimura, K., Hu, Z.: Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features. IEICE Transactions on Information and Systems 91-D(8), 2153–2162 (2008)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn., pp. 839–842. Pearson Prentice Hall, USA (2008)
Philips, P.J., Moon, H., Risvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Machine Intell. 22(10), 1090–1104 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wijaya, I.G.P.S., Uchimura, K., Koutaki, G. (2011). Fast and Robust Face Recognition for Incremental Data. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22819-3_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-22819-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22818-6
Online ISBN: 978-3-642-22819-3
eBook Packages: Computer ScienceComputer Science (R0)