Abstract
In this paper we present a face model based on learning a relation between local features and a face invariant. We have developed a face invariant model for accurate face localization in natural images that is robust to viewpoints changes. A probabilistic model learned from a training set captures a relationship between features appearance and face invariant geometry. It is then used to infer a face instance in new image. We use the invariant local features which have the high performances of objects appearance distinctiveness. The face appearance features are recognized by EM classification. Then, face invariant parameters are predicted and a hierarchical clustering method achieves invariant geometric localization. The clustering uses an aggregate value to construct clusters of invariants. The face appearance probabilities of features are computed to select the best clusters and thus to localize faces in images. We evaluate our generic invariant by testing it in face detection experiments on PIE, FERET and CMU-Profiles databases. The experimental results show that our face invariant model gives highly accurate face localization.
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References
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press (2000)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: CVPR (2001)
Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. IJCV 60(1), 63–86 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse part-based representation. PAMI 26(11), 1475–1490 (2004)
Yu, G., Morel, J.M.: A Fully Affine Invariant Image Comparison Method. In: Proc. IEEE ICASSP, Taipei (2009)
Fei-Fei, L., Fergus, R., Perona, P.: A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories. In: ICCV, Nice, France, pp. 1134–1141 (2003)
Bart, E., Byvatov, E., Ullman, S.: View-Invariant Recognition Using Corresponding Object Fragments. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 152–165. Springer, Heidelberg (2004)
Pope, A.R., Lowe, D.G.: Probabilistic Models of Appearance for 3-D Object Recognition. IJCV 40(2), 149–167 (2000)
Toews, M., Arbel, T.: Detection over Viewpoint via the Object Class Invariant. In: Proc. ICPR, vol. 1, pp. 765–768 (2006)
Burns, J., Weiss, R., Riseman, E.: View variation of point set and line-segment features. PAMI 15(1), 51–68 (1993)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: CVPR 2003, Madison, Wisconsin, pp. 264–271 (2003)
CMU Face Group, Frontal and profile face databases (2009), http://vasc.ri.cmu.edu/idb/html/face/
Color FERET Face Database (2009), www.itl.nist.gov/iad/humanid/colorferet
Kadir, T., Brady, M.: Saliency, scale and image description. IJCV 45(2), 83–105 (2001)
Dorko, G., Schmid, C.: Selection of scale-invariant parts for object class recognition. In: ICCV, pp. 634–640 (2003)
PIE Database, CMU Pose, Illumination, and Expression (PIE) database (2009), http://www.ri.cmu.edu/projects/project_418.html
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Taffar, M., Miguet, S., Benmohammed, M. (2012). Viewpoint Invariant Face Detection. In: Benlamri, R. (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30567-2_33
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DOI: https://doi.org/10.1007/978-3-642-30567-2_33
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