Abstract
We describe a novel method for real-time, simultaneous multi-view face detection and facial pose estimation. The method employs a convolutional network to map face images to points on a manifold, parametrized by pose, and non-face images to points far from that manifold. This network is trained by optimizing a loss function of three variables: image, pose, and face/non-face label. We test the resulting system, in a single configuration, on three standard data sets – one for frontal pose, one for rotated faces, and one for profiles – and find that its performance on each set is comparable to previous multi-view face detectors that can only handle one form of pose variation. We also show experimentally that the system’s accuracy on both face detection and pose estimation is improved by training for the two tasks together.
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
Bottou, L., LeCun, Y.: The Lush Manual (2002), http://lush.sf.net
Caruana, R.: Multitask learning. Machine Learning 28, 41–75 (1997)
Garcia, C., Delakis, M.: A neural architecture for fast and robust face detection. In: IEEE-IAPR Int. Conference on Pattern Recognition, pp. 40–43 (2002)
Huang, F.J., LeCun, Y.: Loss functions for discriminative training of energy-based graphical models. Technical report, Courant Institute of Mathematical Science, NYU (June 2004)
Jones, M., Viola, P.: Fast multi-view face detection. Technical Report TR2003-96, Mitsubishi Electric Research Laboratories (2003)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.: Statistical learning of multi-view face detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 67–81. Springer, Heidelberg (2002)
Li, Y., Gong, S., Liddell, H.: Support vector regression and classification based multi-view face detection and recognition. In: Face and Gesture (2000)
Moon, H., Miller, M.L.: Estimating facial pose from sparse representation. In: International Conference on Image Processing, Singapore (2004)
Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: CVPR (1994)
Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. PAMI 20, 22–38 (1998)
Rowley, H.A., Baluja, S., Kanade, T.: Rotation invariant neural network-based face detection. In: Computer Vision and Pattern Recognition (1998)
Schneidermn, H., Kanade, T.: A statistical method for 3d object detection applied to faces and cars. In: Computer Vision and Pattern Recognition (2000)
Sung, K., Poggio, T.: Example-based learning of view-based human face detection. PAMI 20, 39–51 (1998)
Vaillant, R., Monrocq, C., LeCun, Y.: Original approach for the localisation of objects in images. IEE Proc. on Vision, Image, and Signal Processing 141(4), 245–250 (1994)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, pp. 511–518 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Osadchy, M., Le Cun, Y., Miller, M.L. (2006). Synergistic Face Detection and Pose Estimation with Energy-Based Models. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11957959_10
Download citation
DOI: https://doi.org/10.1007/11957959_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68794-8
Online ISBN: 978-3-540-68795-5
eBook Packages: Computer ScienceComputer Science (R0)