Synergistic Face Detection and Pose Estimation with Energy-Based Models
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.
KeywordsFace Detection Equal Error Rate Multitask Learning Synergy Test Pose Estimation
Unable to display preview. Download preview PDF.
- 1.Bottou, L., LeCun, Y.: The Lush Manual (2002), http://lush.sf.net
- 3.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)Google Scholar
- 4.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)Google Scholar
- 5.Jones, M., Viola, P.: Fast multi-view face detection. Technical Report TR2003-96, Mitsubishi Electric Research Laboratories (2003)Google Scholar
- 8.Li, Y., Gong, S., Liddell, H.: Support vector regression and classification based multi-view face detection and recognition. In: Face and Gesture (2000)Google Scholar
- 9.Moon, H., Miller, M.L.: Estimating facial pose from sparse representation. In: International Conference on Image Processing, Singapore (2004)Google Scholar
- 10.Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: CVPR (1994)Google Scholar
- 11.Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. PAMI 20, 22–38 (1998)Google Scholar
- 12.Rowley, H.A., Baluja, S., Kanade, T.: Rotation invariant neural network-based face detection. In: Computer Vision and Pattern Recognition (1998)Google Scholar
- 13.Schneidermn, H., Kanade, T.: A statistical method for 3d object detection applied to faces and cars. In: Computer Vision and Pattern Recognition (2000)Google Scholar
- 14.Sung, K., Poggio, T.: Example-based learning of view-based human face detection. PAMI 20, 39–51 (1998)Google Scholar
- 16.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)Google Scholar