Abstract
Automatic face recognition is a challenge task, especially working in practical uncontrolled environments. Over the past two decades, numerous innovative ideas and effective processing approaches had been proposed and developed, e.g. various normalization techniques, intrinsic feature extractions and representation schemes, machine learning methods and recognition mechanisms etc. Those approaches based on different principles had been shown possessing varying degrees of effectiveness in different aspects. It is expected that the techniques of information fusion with integrating the advantages of existing methods will boost the recognition performance. This paper deals with developing effective approaches for face recognition using information fusion techniques based on integrating multiple cues. The multiple stage integrating techniques dedicated to localization of landmark points and pose estimation were presented. The precise data of localization of landmarks and pose estimation provide the essential geometry basics for further processing. A face recognition classifier scheme with integration of multiple feature representation and multiple block region scores is also proposed. The experiment results show that the proposed approach can reduce equal error rate EER significantly, compared with using single feature and single block representations. The proposed approach had been shown possessing the best performance in participating MCFR2011 competition.
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Zhao, W., Chellappa, R., Phillips, P. J., et al. (2003). Face recognition: a literature survey [J]. ACM Computing Survey, 35(4), 399–458.
Phillips, P.J., Scruggs, W.T., O’Toole, A.J., Flynn, P.J., Bowyer, K.W., Schott, C.L., et al. (2007). FRVT 2006 and ICE 2006 large-scale results.Technical Report, National Institute of Standards and Technology.
He, X. F., Yan, S. C., Hu, Y. X., et al. (2005). Face recognition using laplacianfaces [J]. IEEE Transactions on Patten Analysis and Machine Intelligence, 27(3), 328–340.
Wright, J., Yang, A., Ganesh, A., Sastry, S. S., & Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 31, 210–227.
Wang, P., Green, M. B., Ji, Q., & Wayman, J. (2005). Automatic eye detection and its validation. San Diego: IEEE Workshop on Face Recognition Grand Challenge Experiments (with CVPR).
Valenti, R., & Gevers, T. (2008). Accurate eye center location and tracking using isophote curvature. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
Asteriadis, S., Nikolaidis, N., Hajdu, A., Pitas, I. (2006). An eye detection algorithm using pixel to edge information. International Symposium on Control, Communications, and Signal Processing.
Jesorsky, O., Kirchbergand, K. J., Frischholz, R. (1992). Robust face detection using the Hausdorff distance. In Audio-and video-based biometric person authentication (pp. 90–95).
Cristinacce, D., Cootes, T., Scott, I. (2004). A multi-stage approach to facial feature detection. In British Machine Vision Conference (pp. 277–286).
Turkan, M., Pardas, M., Cetin, A. (2007). Human eye localization using edge projection. In International Conference on Computer Vision Theory and Applications.
Bai, L., Shen, L., Wang, Y. (2006). A novel eye location algorithm based on radial symmetry transform. In Proceedings of the 18th International Conference on Pattern Recognition (pp. 511–514).
Campadelli, P., Lanzarotti, R, Lipori, G. (2006). Precise eye localization through a general-to-specific model definition. In British Machine Vision Conference (pp. 187–196).
Hamouz, M., Kittlerand, J., Kamarainen, J. K., Paalanen, P., Kalviainen, H., & Matas, J. (2005). Feature-based affine-invariant localization of faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 1490–1495.
Han, Z. C., Su, T. M., Ou, Z. Y., & Xu, W. J. (2012). Precise localization of eye centers with multiple cues. Multimedia tools and applications. doi:10.1007/s11042-012-1090-4.
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features [C], In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 511–518).
Viola, P., & Jones, M. (2004). Robust real–time face detection [J]. International Journal of Computer Vision, 57(2), 137–154.
Candes, E. J., & Donoho, D. L. (2000). Curvelets–a surprisingly effective nonadaptive representation for objects with edges [C]. In C. Rabut, A. Cohen, & L. Schumaker (Eds.), Curves and surface fitting: Saint-Malo 1999 (pp. 105–120). Nashville: Vanderbilt University Press, Nashville.
Candes, E. J., & Guo, F. (2002). New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction [J]. Signal Processing, 82, 1519–1543.
Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. SIENCE, 290(5500), 2323–2326.
Gao, W., Cao, B., Shan, S.G., Zhou, D.L., Zhang, X.H., Zhao, D.B. (2004). The CAS-PEAL large-scale chinese face database and baseline evaluations. http://www.jdl.ac.cn/peal/files/TechReport4-CAS-PEAL-R1.pdf
Donoho, D. L. (2006). For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparest solution. Communications on Pure and Applied Mathematics, 59(6), 797–829.
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227.
http://www/aiar.xjtu.edu.cn/groups/face/Chinese/Homepage.htm
Shafi M., Iqbal F., Ali I. (2011). Face pose estimation using distance transform and normalized cross-correlation. In Proceedings of IEEE International Conference on Signal and Image Processing Applications (pp. 186–191).
Sun, Q. S., Zeng, S. G., Liu, Y., et al. (2005). A new method of feature fusion and its application in image recognition [J]. Pattern Recognition, 38(12), 2437–2448.
Ojala, T., Pietikainen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1), 51–59.
Kittler, J., Hatef, M., Duin, R. P. W., & Matas, J. (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 226–239.
Sadefhi, M. T., Samiei, M., & Kittler, J. (2010). Fusion of PCA-based and LDA-based similarity measures for face verification. EURASIP Journal on Advances in Signal Processing. doi:10.1155/2010/647597.
The BioID Face Database. http://www.bioid.com
Phillips, P.J. Overview of the multiple biometric grand challenge. http://face.nist.gov/-mbgc/mbgc_presentations.htm
Acknowledgments
The research work is supported by the research funds of Dalian University of Technology. The authors would like to thank to providers of the following datasets: BioID face dataset, CAS-PEAL face dataset, OFD face dataset and MBGC face dataset.
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Han, Z., Su, T., Tang, X. et al. Face Recognition with Integrating Multiple Cues. J Sign Process Syst 74, 391–404 (2014). https://doi.org/10.1007/s11265-013-0830-7
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DOI: https://doi.org/10.1007/s11265-013-0830-7