Abstract
A novel image-level fusion algorithm is proposed for 3D face recognition, which synthesizes an integrate image from both 2D intensity and 3D depth images. Due to the same descriptors in 2D and 3D domain, the image combination not only maintains facial intrinsic details to the utmost extent, but also provides more distinctive features. Also as the result of image recognition, the low efficiency of 3D surface matching is eliminated, and a fast 3D face recognition system is carried out. After the proposed surface preprocessing, an enhanced ULBP descriptor is applied to reduce the feature dimension, and LDA is adopted to extract the optimal discriminative components from the integrate image. Experiments performed on the FRGC v2.0 show that this algorithm practically outperforms the existing state-of-art multimodel recognition algorithm and realizes a real-time face recognition system.
This is a preview of subscription content, log in via an institution to check access.
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
Bowyer, P.K., Chang, K.: A survey of approaches and challenges in 3d and multi-modal 3d + 2d face recognition. Computer Vision and Image Understanding 101, 1–15 (2006)
Wang, Y., Chua, C.S.: Robust face recognition from 2d and 3d images using structural hausdorff distance. Image and Vision Computing 24, 176–185 (2006)
Mian, A.: Mohammed, Bennamoun, R.Owens: An efficient multimodal 2d-3d hybrid approach to automatic face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1927–1943 (2007)
Husken, M., Brauckmann, M., Gehlen, S.: V.D.Malsburg: Strategies and benefits of fusion of 2d and 3d face recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 3, pp. 174–182. IEEE, San Diego (2005)
Chang, K.I., Bowyer, K.W., Flynn, P.J.: An evaluation of multimodal 2d+3d face biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 619–624 (2005)
Xu, C.: Stan Li, T.Tan: Automatic 3d face recognition from depth and intensity gabor features. Pattern Recognition 42, 1895–1905 (2009)
Mian, A.S., Bennamoun, M., Owens, R.: Keypoint detection and local feature matching for textured 3d face recognition. International Journal of Computer Vision 79, 1–12 (2008)
Huang, D., Ardabilian, M., Wang, Y., Chen, L.: Automatic asymmetric 3d-2d face recognition. In: The Twentieth International Conference on Pattern Recognition, IAPR, Istanbul (2010)
Besl, P., Mckay, N.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 239–256 (1992)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Belhumur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Papatheodorou, T., Reuckert, D.: Evaluation of automatic 4D face recognition using surface and texture registration. In: The Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 321–326. IEEE, Seoul (2004)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W., Phillips, P.J.: Overview of the face recognition grand challenge. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 3, pp. 947–954. IEEE, San Diego (2005)
Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.V.: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 775–779 (1997)
Wang, Y., Tang, X., Liu, J., Pan, G., Xiao, R.: 3D Face Recognition by Local Shape Difference Boosting. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 603–616. Springer, Heidelberg (2008)
Xu, C., Wang, Y., Tan, T., Quan, L.: A robust method for detecting nose on 3d point cloud. Pattern Recognition Letters 27, 1487–1497 (2006)
Chang, K.I., Bowyer, W., Flynn, P.J.: Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1695–1700 (2006)
Bronstein, E.M., Bronstein, M.M., Kimmel, R.: Three-dimensional face recognition. International Journal of Computer Vision 64, 5–30 (2005)
Maurer, T., Guigonis, D., Maslov, I., Pesenti, B., Tsaregorodtsev, A., West, D., Medioni, G.: Performance of geometrix ActiveIDTM 3D face recognition engine on the FRGC data. In: IEEE Workshop on Face Recognition Grand Challenge Experiments, pp. 154–160. IEEE, Los Alamitos (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiong, P., Huang, L., Liu, C. (2011). Real-Time 3D Face Recognition with the Integration of Depth and Intensity Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-21596-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21595-7
Online ISBN: 978-3-642-21596-4
eBook Packages: Computer ScienceComputer Science (R0)