Abstract
We present in this paper a precise eye detection method using Discriminating Histograms of Oriented Gradients (DHOG) features. The DHOG feature extraction starts with a Principal Component Analysis (PCA) followed by a whitening transformation on the standard HOG feature space. A discriminant analysis is then performed on the reduced feature space. A set of basis vectors, based on the novel definition of the within-class and between-class scatter vectors and a new criterion vector, is defined through this analysis. The DHOG features are derived in the subspace spanned by these basis vectors. Experiments on Face Recognition Grand Challenge (FRGC) show that (i) DHOG features enhance the discriminating power of HOG features and (ii) our eye detection method outperforms existing methods.
Keywords
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
Campadelli, P., Lanzarotti, R., Lipori, G.: Precise eye localization through a general-to-specific model definition. In: British Machine Vision Conference (2006)
Chen, S., Liu, C.: Eye detection using color information and a new efficient svm. In: IEEE Int. Conf. on Biometrics: Theory, Applications and Systems (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Eckhardt, M., Fasel, I., Movellan, J.: Towards practical facial feature detection. Internatioanl Journal of Pattern Recognition and Artificial Intelligence 23(3), 379–400 (2009)
Fukunaga, K.: Introduction to statistical pattern recognition. Academic Press, London (1990)
Jin, L., Yuan, X., Satoh, S., Li, J., Xia, L.: A hybrid classifier for precise and robust eye detection. In: IEEE Int. Conf. on Pattern Recognition (2006)
Kroon, B., Maas, S., Boughorbel, S., Hanjalic, A.: Eye localization in low and standard definition content with application to face matching. Computer Vision and Image Understanding 113(4), 921–933 (2009)
Liu, C.: A Bayesian discriminating features method for face detection. IEEE Trans. Pattern Analysis and Machine Intelligence 25(6), 725–740 (2003)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Nguyen, M., Perez, J., Frade, F.: Facial feature detection with optimal pixel reduction svm. In: IEEE International Conference on Automatic Face and Gesture (2008)
Phillips, P., Flynn, P., Scruggs, T.: Overview of the face recognition grand challenge. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition (2005)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The feret evaluation methodology for face recognition algorithms. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Wang, P., Green, M., Ji, Q., Wayman, J.: Automatic eye detection and its validation. In: IEEE International Conference on Computer Vision and Pattern Recognition (2005)
Wang, P., Ji, Q.: Multi-view face and eye detection using discriminant features. Computer Vision and Image Understanding 105(2), 99–111 (2007)
Zhu, Z., Ji, Q.: Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Computer Vision and Image Understanding 98(1), 124–154 (2005)
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
Chen, S., Liu, C. (2011). Precise Eye Detection Using Discriminating HOG Features. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-23672-3_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23671-6
Online ISBN: 978-3-642-23672-3
eBook Packages: Computer ScienceComputer Science (R0)