Abstract
Frequency domain 3D-filter designing for automatic face detection and neural network based searching algorithm for eye localization of detected faces in video sequences is proposed. A series of spatiotemporal volumes are constructed from the video sequences of faces by concatenating the frames of a single complete cycle of face position is used to design a 3D unconstrained correlation filter by classical Fourier approach. The Unconstrained Optimal Trade-off Synthetic Discriminant Function (UOTSDF) filter is generalised here into a video filter of 3D spatio-temporal volume. After extracting the facial region by 3D correlation filter in frequency domain of the video frames, a neural network is employed to locate the eyes. The novelty of the face detection in video by frequency domain analysis and fast eye searching by parallel neural net of Generalised Regression Neural Network (GRNN) is validated with the benchmark database like VidTIMIT video database.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Mahalanobis, A., Kumar, B.V.K.V., Casassent, D.: Minimum average correlation energy filter. Applied Optics 26 (1987)
Mahalanobis, A., Kumar, B.V., Song, S., Sims, S., Epperson, J.: Unconstrained correlation filter. App.Opt. 33, 3751–3759 (1994)
Heisele, B., Poggio, T., Pontil, M.: Face detection in still gray images. Tech. rep., Artificial Intelligence Laboratory, MIT (2000)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)
Kumar, B.V.K.V.: Minimum variance synthetic discriminant functions. J. Opt. Soc. Am. 3 (1986)
Catalano, G., Gallace, A.: B.Kim, Pedro, S., F.Santoro: Optical flow. Tech. rep. (March 2009), http://www.cvmt.dk/education/teaching/f09/VGIS8/AIP/
Specht, D.F.: A general regression neural network. IEEE Transaction on Neural Networks 2, 568 (1991)
Maio, D., Maltoni, D.: Real-time face location on grayscale static images. Pattern Recognition 33, 1525–1539 (2000)
Figue, J., Rfrgier, P.: Optimality of trade-off filters. Applied Optics 32(11), 1933–1935 (1993)
Rowley, H.A., Baluja, S., Kanade, T.: Neural network based face detection. IEEE Trans. Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)
Heo, J., Savvides, M., Abiantun, R., Xie, C.: Face recognition with kernel correlation filters on a large scale database. In: Proc. of IEEE Int. Conf. on Acoustics,Speech and Signal Processing, vol. II, p. 181 (2006)
Yang, J., Waibel, A.: A real time face tracker. In: Proc. of the Third IEEE Workshop on Applications of Computer Vision, pp. 142–147 (1996)
Mikolajczyk, K., Choudhury, R., Schimd, C.: Face detection in a video sequence -a temporal approach. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 96–101 (2001)
Silva, L., Aizawa, K., Hatori, M.: Detection and tracking of facial features. In: Proc. of SPIE Visual Communications and Image Processing, Taiwan
Rodriguez, M.D.: Ahmed, J., M.Shah: Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: IEEE conference on Computer Vision and Pattern Recognition, pp. 1–8 (June 2008)
Savvides, M., Kumar, B.V., Khosla, P.K.: Robust shift invariant biometric identification from partial face images. In: Proc. of SPIE Defense and Security Symposium, vol. 156 (2004)
M.Savvides, Venkataramani, Kumar, B.: Incremental updating of advanced correlation filters for biometric authentication systems. In: Proc. of IEEE Int. Conf. on Multimedia and Expo. vol. III, p. 229 (2003)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. Conf. Computer Vision and Pattern Recognition. pp. 511–518 (2001)
Sanderson, C., Paliwal, K.: Polynomial features for robust face authentication. In: IEEE International Conference on Image Processing (ICIP). vol. 3, pp. 997–1000 (2002), http://itee.uq.edu.au/~conrad/vidtimit/
Xu, T.-Q., Li, B.C., Wang, B.: Face detection and recognition using neural network and hidden markov models. In: Proccedings of the 2003 International Conference on Neural Networks and Signal Processing, pp. 228–231 (2003)
Liu, Z., Wang, Y.: Face detection and tracking in video using dynamic programming. In: Proc. International Conference Image Processing (2000)
Zhang, Z., Potamianos, G., Liu, M., Huang, T.: Robust multi-view multi-camera face detection inside smart rooms using spatio temporal dynamic programming. In: 7th International Conference on Automatic Face and Gesture Recognition, pp. 407–412 (April 2006)
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
Banerjee, P.K., Chandra, J.K., Datta, A.K. (2011). Face Detection and Eye Localization in Video by 3D Unconstrained Filter and Neural Network. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22714-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-22714-1_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22713-4
Online ISBN: 978-3-642-22714-1
eBook Packages: Computer ScienceComputer Science (R0)