Abstract
We present a component-based framework for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier. The component classifiers independently detect/identify facial parts in the image. Their outputs are passed the combination classifier which performs the final detection/identification of the face.
We describe an algorithm which automatically learns two separate sets of facial components for the detection and identification tasks. In experiments we compare the detection and identification systems to standard global approaches. The experimental results clearly show that our component-based approach is superior to global approaches.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Beymer, D.J. 1993. Face recognition under varying pose. Center for Biological and Computational Learning, M.I.T., Cambridge, MA, A.I. Memo 1461.
Bileschi, S.M. and Heisele, B. 2002. Advances in component-based face detection. In Proceedings of Pattern Recognition with Support Vector Machines, First International Workshop, SVM 2002, Niagara Falls, pp. 135–143.
Blanz, V. and Vetter, T. 1999. A morphable model for synthesis of 3D faces. In Computer Graphics Proceedings SIGGRAPH, Los Angeles, pp. 187–194.
Brunelli, R. and Poggio, T. 1993. Face recognition: Features versus templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10):1042–1052.
Dorko, G. and Schmid, C. 2003. Selection of scale invariant neighborhoods for object class recognition. In International Conference on Computer Vision (ICCV), pp. 634–640.
Fergus, R., Perona, P., and Zisserman, A. 2003. Object class recognition by unsupervised scale-invariant learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–271.
Gross, R. 2005. Face databases. In Handbook of Face Recognition, A. S. Li (ed.), Springer, New York.
Heisele, B., Ho, P., Wu, J., and Poggio, T. 2003. Face recognition: Component-based versus global approaches. Computer Vision and Image Understanding (CVIU) 91(1–2):6–21.
Heisele, B., Serre, T., Mukherjee, S., and Poggio. T. 2003. Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition, 36(9):2007–2017.
Heisele, B., Serre, T., Pontil, M., Vetter, T., and Poggio, T. 2001. Categorization by learning and combining object parts. In Neural Information Processing Systems (NIPS), Vancouver.
Ivanov, Y., Heisele, B., and Serre, T. 2004. Using component features for face recognition. In Proceedings of the 6th International Conference on Automatic Face and Gesture Recognition, pp. 421–426.
Lienhart, R., Kuranov, A., and Pisarevsky, V. 2003. Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In DAGM'03, 25th Pattern Recognition Symposium, pp. 297–304.
Lowe, D.G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110.
Martinez, A.M. 2002. Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6):748–763.
Mohan, A., Papageorgiou, C., and Poggio, T. 2001. Example-based object detection in images by components. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:349–361.
Morgenstern, C. and Heisele, B. 2003. Component-based recognition of objects in an office environment. Center for Biological and Computational Learning, M.I.T., Cambridge, MA, A.I. Memo 232.
Nefian, A. and Hayes, M. 1999. An embedded HMM-based approach for face detection and recognition. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3553–3556.
Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., and Poggio. T. 1997. Pedestrian detection using wavelet templates. In IEEE Conference on Computer Vision and Pattern Recognition, San Juan, pp. 193–199.
Osuna, E. 1998. Support vector machines: Training and applications. Ph.D. dissertation, MIT, Department of Electrical Engineering and Computer Science, Cambridge, MA.
OpenCV Online Reference Manual. 2006. The Intel Corporation.
Pentland, A., Mogghadam, B., and Starner, T. 1994. View-based and modular eigenspaces for face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Phillips, P.J. 1998. Matching pursuit filters applied to face identification. IEEE Transactions on Image Processing, 7(8):1150–1164.
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., and Worek, W. 2005. Overview of the face recognition grand challenge. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 947–954.
Poggio, T. and Edelman, S. 1990. A network that learns to recognize 3-D objects. Nature, 343:163–266.
Riesenhuber, M. and Poggio, T. 1999. Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11):1019–1025.
Rowley, H.A., Baluja, S., and Kanade. T. 1998. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):23–38.
Schneiderman, H. and Kanade, T. 2000. A statistical method for 3D object detection applied to faces and cars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 746–751.
Serre, T., Wolf, L., and Poggio, T. 2005. Object recognition with features inspired by visual cortex. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 994–1000.
Sim, T., Baker, S., and Bsat, M. 2003. The CMU pose, illumination, and expression database. IEEE Transactions Pattern Analysis and Machine Intelligence (PAMI) 25(12):1615–1618.
Sung, K.-K. 1996. Learning and example selection for object and pattern recognition. Ph.D. dissertation, MIT, Artificial Intelligence Laboratory and Center for Biological and Computational Learning, Cambridge, MA.
Ullman, S., Vidal-Naquet, M., and Sali, E. 2002. Visual features of intermdediate complexity and their use in classification. Nature Neuroscience, 5(7):682–687.
Vetter, T. 1998. Synthesis of novel views from a single face. International Journal of Computer Vision, 28(2):103–116.
Viola, P. and Jones, M.J. 2004. Robust real-time face detection. International Journal of Computer Vision, 57(2):137–154.
Weber, M., Welling, W., and Perona, P. 2000. Towards automatic dscovery of object categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Wiskott, L., Fellous, J.-M., Krüger, N., and von der Malsburg, C. 1997. Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):775 –779.
Zhao, W., Chellappa, R., Phillips, P.J., and Rosenfeld, A. 2003. Face recognition: A literature survey. ACM Comput. Surv., 35(4):399–458.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Heisele, B., Serre, T. & Poggio, T. A Component-based Framework for Face Detection and Identification. Int J Comput Vision 74, 167–181 (2007). https://doi.org/10.1007/s11263-006-0006-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11263-006-0006-z