Abstract
The alignment of the facial region with a predefined canonical form is one of the most crucial steps in a face recognition system. Most of the existing alignment techniques rely on the position of the eyes and, hence, require an efficient and reliable eye localization procedure. In this paper we propose a novel technique for this purpose, which exploits a new class of correlation filters called Prinicpal directions of Synthetic Exact Filters (PSEFs). The proposed filters represent a generalization of the recently proposed Average of Synthetic Exact Filters (ASEFs) and exhibit desirable properties, such as relatively short training times, computational simplicity, high localization rates and real time capabilities. We present the theory of PSEF filter construction, elaborate on their characteristics and finally develop an efficient procedure for eye localization using several PSEF filters. We demonstrate the effectiveness of the proposed class of correlation filters for the task of eye localization on facial images from the FERET database and show that for the tested task they outperform the established Haar cascade object detector as well as the ASEF correlation filters.
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
Bolme, D.S., Draper, B.A., Beveridge, J.R.: Average of synthetic exact filters. In: Proc. of CVPR 2009, pp. 2105–2112 (2009)
Bolme, D.S., Liu, Y.M., Draper, B.A., Beveridge, J.R.: Simple real-time human detection using a single correlation filter. In: Proc. of the 12th Workshop on Performance Evaluation of Tracking and Surveillance, pp. 1–8 (2009)
Bradski, G., Kaehler, A.: Learning OpenCV: computer vision with the OpenCV library. O’Reilly Media, Sebastopol (2008)
Hester, C.F., Casasent, D.: Mulitvariant technique for multiclass pattern recognition. Applied Optics 19(11), 1758–1761 (1980)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled Faces in the Wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report 07-49 (October 2007)
Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)
Kerekes, R.A., Kumar, B.V.K.V.: Correlation filters with controlled scale response. IEEE Transactions on Image Processing 15(7), 1794–1802 (2006)
Kumar, B.V.K.V., Mahalanobis, A., Takessian, A.: Optimal tradeoff circular harmonic function correlation filter methods providing controlled in-plane rotation response. IEEE Transactions on Image Processing 9(6), 1025–1034 (2000)
Mahalanobis, A., Kumar, B.V.K.V., Casasent, D.: Minimum average correlation energy filters. Applied Optics 26(17), 3633–3640 (1987)
Mahalanobis, A., Kumar, B.V.K.V., Sims, S.R.F.: Distance-classifier correlation filters for multiclass target recognition. Applied Optics 35(17), 3127–3133 (1996)
Mahalanobis, A., Kumar, B.V.K.V., Song, S., Sims, S.R.F., Epperson, J.: Unconstrained correlation filters. Applied Optics 33(17), 3751–3759 (1994)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Refregier, P.: Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency. Optics Letters 16(11), 829–831 (1991)
Savvides, M., Kumar, B.V.K.V.: Efficient design of advanced correlation filters for robust distortion-tolerant face recognition. In: Proc. of the IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 45–52 (2003)
Štruc, V., Gajšek, R., Pavešić, N.: Principal Gabor filters for face recognition. In: Proc. of BTAS 2009, pp. 1–6 (2009)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neurosicence 3(1), 71–86 (1991)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)
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
Štruc, V., Gros, J.Ž., Pavešić, N. (2011). Principal Directions of Synthetic Exact Filters for Robust Real-Time Eye Localization. In: Vielhauer, C., Dittmann, J., Drygajlo, A., Juul, N.C., Fairhurst, M.C. (eds) Biometrics and ID Management. BioID 2011. Lecture Notes in Computer Science, vol 6583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19530-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-19530-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19529-7
Online ISBN: 978-3-642-19530-3
eBook Packages: Computer ScienceComputer Science (R0)