Abstract
We propose an automatic pose invariant approach for Face Recognition At a Distance (FRAD). Since face alignment is a crucial step in face recognition systems, we propose a novel facial features extraction model, which guides extended ASM to accurately align the face. Our main concern is to recognize human faces under uncontrolled environment at far distances accurately and fast. To achieve this goal, we perform an offline stage where 3D faces are reconstructed from stereo pair images. These 3D shapes are used to synthesize virtual 2D views in novel poses. To obtain good synthesized images from the 3D shape, we propose an accurate 3D reconstruction framework, which carefully handles illumination variance, occlusion, and the disparity discontinuity. The online phase is fast where a 2D image with unknown pose is matched with the closest virtual images in sampled poses. Experiments show that our approach outperforms the-state-of-the-art approaches.
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References
Zhang, X., Gao, Y.: Face recognition across pose: A review. Pattern Recogn. 42 (2009)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 681–685 (2001)
Guillemaut, J.-Y., Kittler, J., Sadeghi, M.T., Christmas, W.J.: General Pose Face Recognition Using Frontal Face Model. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 79–88. Springer, Heidelberg (2006)
Gao, H., Ekenel, H.K., Stiefelhagen, R.: Pose Normalization for Local Appearance-Based Face Recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 32–41. Springer, Heidelberg (2009)
Chai, X., Shan, S., Chen, X., Gao, W.: Locally linear regression for pose-invariant face recognition. IEEE Transactions on Image Processing 16, 1716–1725 (2007)
Saquib Sarfraz, M., Hellwich, O.: Probabilistic learning for fully automatic face recognition across pose. Image Vision Comput. 28, 744–753 (2010)
Asthana, A., Jones, M., Marks, T., Tieu, K., Goecke, R.: Pose normalization via learned 2D warping for fully automatic face recognition. In: Proceedings of the BMVC, pp. 127.1–127.11 (2011)
Castillo, C., Jacobs, D.: Using stereo matching with general epipolar geometry for 2D face recognition across pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 2298–2304 (2009)
Zhang, X., Gao, Y., Leung, M.: Automatic texture synthesis for face recognition from single views. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1151–1154 (2006)
Liu, X.: Pose-robust face recognition using geometry assisted probabilistic modeling. In: Proceedings of CVPR, vol. 1, pp. 502–509 (2005)
Mostafa, E.A., Farag, A.A.: Dynamic weighting of facial features for automatic pose-invariant face recognition. In: 2012 Ninth Conference on Computer and Robot Vision (CRV), pp. 411–416 (2012)
Hesher, C., Srivastava, A., Erlebacher, G.: A novel technique for face recognition using range imaging. In: Proc. of 7th Int. Symp. on Sig. Proc. and Its App., pp. 201–204 (2003)
Ahmed, A., Farag, A.: A New Statistical Model Combining Shape and Spherical Harmonics Illumination for Face Reconstruction. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part I. LNCS, vol. 4841, pp. 531–541. Springer, Heidelberg (2007)
Park, U., Jain, A.K.: 3D face reconstruction from stereo video. In: Proc. of First International Workshop on Video Processing for Security (2006)
Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1063–1074 (2003)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 643–660 (2001)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 2037–2041 (2006)
Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2001)
Milborrow, S., Nicolls, F.: Locating Facial Features with an Extended Active Shape Model. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 504–513. Springer, Heidelberg (2008)
Cristinacce, D., Cootes, T., Scott, I.: A multi-stage approach to facial feature detection. In: 15th BMVC, London, England, pp. 277–286 (2004)
Valstar, M., Martinez, B., Binefa, X., Pantic, M.: Facial point detection using boosted regression and graph models. In: CVPR 2010, San Francisco, USA, pp. 2729–2736 (2010)
Everingham, M., Sivic, J., Zisserman, A.: “Hello! My name is... Buffy” – automatic naming of characters in TV video. In: Proceedings of the British Machine Vision Conference (2006)
Dryden, I., Mardia, K.V.: The statistical analysis of shape. Wiley, London (1998)
Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2011)
Mostafa, E., El-Melegy, M., Farag, A.A.: Passive single image-based approach for camera steering in face recognition at a distance application. In: Proc. of IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS (2012)
Kolmogorov, V., Zabih, R.: Multi-camera Scene Reconstruction via Graph Cuts. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 82–96. Springer, Heidelberg (2002)
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A Comparative Study of Energy Minimization Methods for Markov Random Fields. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part II. LNCS, vol. 3952, pp. 16–29. Springer, Heidelberg (2006)
Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. on PAMI 20, 401–406 (1998)
Finlayson, G., Xu, R.: Illuminant and gamma comprehensive normalisation in log RGB space. Pattern Recogn. Lett. 24, 1679–1690 (2003)
Heo, Y., Lee, K., Lee, S.: Illumination and camera invariant stereo matching. In: Proc. of CVPR (2008)
Abdelrahman, M., Ali, A.M., Elhabian, S., Rara, H., Farag, A.A.: A passive stereo system for 3D human face reconstruction and recognition at a distance. In: WACV (2012)
Mostafa, E., El-Barkouky, A., Rara, H., Farag, A.A.: Rejecting Pseudo-Faces using the Likelihood of Facial Features and Skin. In: Proc. of IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS (2012)
Kanade, T., Yamada, A.: Multi-subregion based probabilistic approach toward pose-invariant face recognition. In: Proceedings of the 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, vol. 2, pp. 954–959 (2003)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: ICCV 2005, vol. 1 (2005)
Asthana, A., Gedeon, T., Goecke, R., Sanderson, C.: Learning-based face synthesis for pose-robust recognition from single image. In: British Machine Vision Conference (2009)
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Mostafa, E., Ali, A., Alajlan, N., Farag, A. (2012). Pose Invariant Approach for Face Recognition at Distance. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33783-3_2
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