Abstract
Some pose invariant face recognition approaches require preprocessing such as face alignment or landmark fitting, which is another unresolved problem. SIFT based face recognition schemes could resolve the problem of constrained pose variation without such preprocessing. we find that the sift descriptors are robust to off-plane rotation within 25 degree and in-plane rotation. Furthermore, we propose complete pose binary SIFT (CPBS) to address the issue of arbitrary pose variation. First, five face images with poses of frontal view, rotation left/right 45 and 90 degree respectively are selected as gallery images of a subject. Then the binary descriptors of these images are pooled together as CPBS of the subject. Face recognition is finished by hamming distance between the probe face image and the CPBS. Experimental results on the CMU-PIE and FERET face databases show that our approach has performance comparable to state-of-the-art approaches, while not requiring face alignment or landmark fitting.
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
Li, A., Shan, S., Gao, W.: Coupled Bias Variance Trade off for Cross-Pose Face Recognition. IEEE Transactions on Image Processing 21(1), 305–315 (2012)
González-Jiménez, D., Alba-Castro, J.L.: Toward pose invariant 2-D face recognition through point distribution models and facial symmetry. IEEE Transactions on Information Forensics and Security (2007)
Prince, S.J.D., Warrell, J., Elder, J.H., Felisberti, F.M.: Tied factor analysis for face recognition across large pose differences. IEEE Trans. Pattern Anal. Mach. Intell 30(6), 970–984 (2008)
Wang, Z., Ding, X., Fang, C.: Pose Adaptive LDA Based Face Recognition. In: ICPR, pp. 1–4 (2008)
Asthana, A., Marks, T., Jones, M., Tieu, K.: Fully automatic pose-invariant face recognition via 3D pose normalization. In: Proc. Int. Conf. Comput., pp. 937–944 (2011)
Ho, H.T., Chellappa, R.: Pose-Invariant Face Recognition Using Markov Random Fields. IEEE Transactions on Image Processing 22(4), 1573–1584 (2013)
Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)
Huang, F.J., Zhou, Z.-H., Zhang, H.-J., Chen, T.: Pose invariant face recognition. In: Proceedings of the 4th IEEE ICAFGR, pp. 245–250 (2000)
Sharma, A., Haj, M.A., Choi, J., Davis, L.S., Jacobs, D.W.: Robust pose invariant face recognition using coupled latent space discriminant analysis. Computer Vision and Image Understanding, 1095–1110 (2012)
Cootes, T.F., Cooper, D., Taylor, C.J., Graham, J.: Active shape models-their training and application. Compute Vision Image Understanding 61(1), 38–59 (1995)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)
Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Zhang, X., Gao, Y.: Face Recognition Across Pose: A Review. Pattern Recognition 42, 2876–2896 (2009)
Du, M., Sankaranarayanan, A.C., Chellappa, R.: Pose-Invariant Face Recognition from Multi-View Videos. IEEE Transactions on Image Processing (2012)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Yu, G., Morel, J.-M.: A fully affine invariant image comparison method. In: ICASSP, pp. 1597–1600 (2009)
Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of SIFT features for face authentication. In: Proc. Of IEEE Int Workshop on Biometrics, in Association with CVPR, pp. 35–41 (2006)
Kisku, D.R., Rattani, A., Grosso, E., Tistarelli, M.: Face Identification by SIFT-based Complete Graph Topology. In: IEEE Workshop on Automatic Identification Advanced Technologies, pp. 63–68 (2007)
Geng, C., Jiang, X.: SIFT features for face recognition. In: Computer Science and Information Technology, pp. 598–602 (2009)
Liu, T., Kim, S.-H., Lee, H.-S., Kim, H.-H.: Face Recognition base on a New Design of Classifier with SIFT keypoints. In: Intelligent Computing and Intelligent Systems, pp. 366–370 (2009)
Rosenberger, C., Brun, L.: Similarity-based matching for face authentication. In: International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)
Luo, J., Ma, Y., Takikawa, E., Lao, S.H., Kawade, M., Lu, B.L.: Person-specific SIFT features for face recognition. In: ICASSP, pp. 563–566 (2007)
Wu, L., Zhou, P., Liu, S., Zhang, X., Trucco, E.: A Face Authentication Scheme Based on Affine-SIFT (ASIFT) and Structural Similarity (SSIM). In: Zheng, W.-S., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds.) CCBR 2012. LNCS, vol. 7701, pp. 25–32. Springer, Heidelberg (2012)
Liao, S., Jain, A.: Partial Face Recognition: An Alignment Free Approach. In: Proc. 2011 IEEE International Joint Conference on Biometrics, pp. 1–8 (2011)
Fischler, M.A., Bolles, R.C.: Random sample consensus:A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Wang, W., Li, H., Wang, M., Lu, Y., Tian, Q.: Binary SIFT: Towards Efficient Feature Matching Verification for Image Search. In: ICIMCS (2012)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET Evaluation Methodology for Face Recognition Algorithms. In: IEEE PAMI, pp. 1090–1104 (2000)
Sim, T., Baker, S.: The CMU Pose, Illumination Expression Database. IEEE PAMI 25(12), 1615–1618 (2003)
Shahdi, S.O., AbuBakar, S.A.R.: Varying Pose Face Recognition Using Combination of Discrete Cosine & Wavelet Transforms. In: International Conference on Intelligent and Advanced Systems, pp. 642–647 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Wu, L., Zhou, P., Hou, Y., Cao, H., Ma, X., Zhang, X. (2013). Complete Pose Binary SIFT for Face Recognition with Pose Variation. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-02961-0_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02960-3
Online ISBN: 978-3-319-02961-0
eBook Packages: Computer ScienceComputer Science (R0)