Fusion Classifier for Open-Set Face Recognition with Pose Variations

Authors: Gee-Sern Jison Hsu

Abstract:

A fusion classifier composed of two modules, one made by a hidden Markov model (HMM) and the other by a support vector machine (SVM), is proposed to recognize faces with pose variations in open-set recognition settings. The HMM module captures the evolution of facial features across a subject-s face using the subject-s facial images only, without referencing to the faces of others. Because of the captured evolutionary process of facial features, the HMM module retains certain robustness against pose variations, yielding low false rejection rates (FRR) for recognizing faces across poses. This is, however, on the price of poor false acceptance rates (FAR) when recognizing other faces because it is built upon withinclass samples only. The SVM module in the proposed model is developed following a special design able to substantially diminish the FAR and further lower down the FRR. The proposed fusion classifier has been evaluated in performance using the CMU PIE database, and proven effective for open-set face recognition with pose variations. Experiments have also shown that it outperforms the face classifier made by HMM or SVM alone.

Keywords: Face recognition, open-set identification, hidden Markov model, support vector machines.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061412

References:

[1] P.J. Phillips, P. Grother, R.J. Micheals, D.M. Blackburn, E. Tabassi, M. Bone, "Face Recognition Vendor Test 2002: Evaluation Report," available at http://www.frvt.org.
[2] V. Blanza and T. Vetter, "Face recognition based on fitting a 3D morphable model," IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), vol. 25, no. 9, pp. 1063-1074, 2003.
[3] H.C. Choi; S.Y Kim; S.H. Oh; S.Y. Oh; S.Y. Cho; "Pose invariant face recognition with 3D morphable model and neural network," Proc. IEEE Int-l J. Conf. Neural Networks (IJCNN), 2008, pp. 4131 - 4136.
[4] Q. Chen, J. Yao, W.K. Cham, "3D model-based pose invariant face recognition from multiple views," Computer Vision, IET, vol.1, i.1, pp. 25 - 34, March 2007.
[5] S. Feng, H. Krim, I. Gu, M. Viberg, "3D Face Recognition Using Affine Integral Invariants," Proc. IEEE Int-l Conf. Acoustics, Speech and Signal Processing (ICASSP) 2006, vol.2, pp. 14-19.
[6] A.F. Abate, M. Nappi, D. Riccio, G. Sabatino, "3D Face Recognition using Normal Sphere and General Fourier Descriptor," Proc. 18th Int-l Conf. Pattern Recognition (ICPR), 2006, vol.3, pp. 1183-1186.
[7] L. Wang, L. Ding, X. Ding, and C. Fang, "Improved 3D assisted poseinvariant face recognition, Proc. ICASSP 2009, pp. 889 - 892.
[8] X. Liu; T. Chen, "Pose-robust face recognition using geometry assisted probabilistic modeling," Proc. IEEE Conf. Computer Vision and Pattern Recognition, (CVPR) 2005, vol.1, pp. 502 - 509.
[9] T. Vetter and T. Poggio, "Linear object classes and image synthesis from a single example image," PAMI, vol.19, no.7, pp.733-742, 1997.
[10] T. Vetter, "Synthesis of novel views from a single face image," Int-l J. Computer Vision (IJCV), vol.28, no.2, 1998, pp.103-116.
[11] X. Chai, S. Shan, X. Chen and W. Gao, "Locally Linear Regression for Pose-Invariant Face Recognition," IEEE Trans. Image Processing, vol. 16, Iss.7, pp. 1716-1725, 2007.
[12] A.V. Nefian and M.H. Hayes III, "Hidden Markov Models for Face Recognition," Proc. ICASSP, 1998, pp. 2721-2724.
[13] A.V. Nefian and M.H. Hayes III, "An Embedded HMM Based Approach for Face Detection and Recognition," Proc. ICASSP, vol.6, 1999, pp. 3553-3556.
[14] S. Eickeler, S. M├╝ller, and G. Rigoll, "Improved Face Recognition using Pseudo 2-D Hidden Markov Models," in Workshop on Advances in Facial Image Analysis and Recognition Technology (AFIART), Freiburg, Germany, 1998.
[15] F. Samaria and S. Young, "HMM-based architecture for face identification," Image and Vision Computing, 12(8), pp. 537-543, 1994.
[16] P.J. Phillips, "Support Vector Machines Applied to Face Recognition," in Advances in Neural Information Processing Systems, vol.11, M.J. Kearns et. al., eds., MIT Press, 1999.
[17] K. Jonsson, J. Matas, J. Kittler, and Y.P. Li, "Learning Support Vectors for Face Verification and Recognition," Proc. IEEE Int-l Conf. on Automatic Face and Gesture Recognition (FG), 2000, pp. 208-213.
[18] B. Heisele, P. Ho, and T. Poggio, "Face Recognition with Support Vector Machines: Global versus Component-Based Approach," Computer Vision and Image Understanding (CVIU), vol.91, no. 1/2, pp. 6-21, 2003.
[19] B. Heisele, T. Serre and T. Poggio, "A Component-based Framework for Face Detection and Identification," IJCV, vol.74, no.2, pp. 167-181, 2007.
[20] P.H. Lee, Y.W. Wang, J. Hsu and Y.P. Hung, "Facial Features Extracted by 2-D HMM for Face Recognition with Pose Variations," Proc. of IAPR Conference on Machine Vision Applications (MVA), pp. 392~395, 2007.
[21] L. R. Rabiner, "A Tutorial on Hidden Markov Models and Se-lected Applications in Speech Recognition," Proc. of IEEE, vol. 77, no. 2, pp. 257-286, 1989.
[22] C.J.C. Burges, "Simplified Support Vector Decision Rules," Proc. 13th Int Conf. on Machine Learning, pp. 71-78, 1996.
[23] T. Sim, S. Baker, and M. Bsat, "CMU pose illumination and expression( PIE) database," PAMI, IEEE Trans, vol.25, NO.12, Dec 2003. pp. 1613 - 1618, 2003.
[24] Frgc P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, M. Jaesik, W. Worek, "Overview of the Face Recognition Grand Challenge," CVPR 2005, vol.1, 20-25, pp.947-954.
[25] P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, W. Worek, " Preliminary Face Recognition Grand Challenge Results," Proc. 7th Int-l Conf Automatic Face and Gesture Recognition, pp. 15-24, 2006.
[26] A.M. Martinez and R. Benavente, "The AR Face Database," CVC Technical Report #24 , June 1998.
[27] K. Messar, J. Matas, and J. Kittler, "XM2VTSDB: The Extended M2VTS Database, " Proc. 2nd Int-l Conf. Audio and Video-based Biometric Person Authentication (AVBPA-99-), pp. 2 - 14, 1999.