Abstract
In general, a face analysis relies on the face orientation; therefore, face orientation discrimination is very important for interpreting the situation of people in an image. In this paper, we propose an enhanced approach that is robust to the unwanted variation of the image such as illumination, size of faces, and conditions of picture taken. In addition to the conventional algorithm (Principal Component Analysis and Independent Component Analysis), we imposed the Gabor kernels and Fourier Transform to improve the robustness of the proposed approach. The experimental results validate the effectiveness of the proposed algorithm for five kinds of face orientation (front, quarter left, perfect left, quarter right, and perfect right side of faces). In real application, the proposed algorithm will enable a Human-Computer Interface (HCI) system to understand the image better by extracting reliable information of face orientation.
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.
Similar content being viewed by others
References
Kurata, D., Nankaku, Y., Tokuda, K., Kitamura, T., Ghahramani, Z.: Face Recognition Based on Separable Lattice HMMs. In: 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. V–V. IEEE Press, New York (2006)
Darrell, T., Moghaddam, B., Pentland, A.P.: Active Face Tracking and Pose Estimation in an Interactive Room. In: 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 67–67. IEEE Press, New York (1996)
Chang, C.C., Aghajanet, H.: Collaborative Face Orientation Detection in Wireless Image Sensor Networks. In: International Workshop on Distributed Smart Cameras (2006)
Dervinis, D.: Head Orientation Estimation using Characteristic Points of Face. Electronics and Electrical Engineering 8(72), 61–64 (2006)
Kaminski, J.Y., Knaan, D., Shavit, A.: Single Image Face Orientation and Gaze Detection. Machine Vision and Applications 21(1), 85–98 (2009)
Rae, R., Ritter, H.J.: Recognition of Human Head Orientation based on Artificial Neural Networks. IEEE Transactions on Neural Networks 9(2), 257–265 (1998)
Liu, C.: Independent Component Analysis of Gabor Features for Face Recognition. IEEE Transactions on Neural Networks 14(4), 919 (2003)
Kailash, J., Karande, N.T., Sanjay, N.T.: Independent Component Analysis of Edge Information for Face Recognition. International Journal of Image Processing 3(3), 120–130 (2009)
Bartlett, M.S.: Face Recognition by Independent Component Analysis. IEEE Transactions on Neural Networks 13(6), 1450 (2002)
FacE REcognition Technology data base information, http://face.nist.gov/colorferet/
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
Song, H.A., Choi, SD., Lee, SY. (2011). Enhanced Discrimination of Face Orientation Based on Gabor Filters. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-24955-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
Online ISBN: 978-3-642-24955-6
eBook Packages: Computer ScienceComputer Science (R0)