Partial Face Matching between Near Infrared and Visual Images in MBGC Portal Challenge
The latest multi-biometric grand challenge (MBGC 2008) sets up a new experiment in which near infrared (NIR) face videos containing partial faces are used as a probe set and the visual (VIS) images of full faces are used as the target set. This is challenging for two reasons: (1) it has to deal with partially occluded faces in the NIR videos, and (2) the matching is between heterogeneous NIR and VIS faces. Partial face matching is also a problem often confronted in many video based face biometric applications.
In this paper, we propose a novel approach for solving this challenging problem. For partial face matching, we propose a local patch based method to deal with partial face data. For heterogeneous face matching, we propose the philosophy of enhancing common features in heterogeneous images while reducing differences. This is realized by using edge-enhancing filters, which at the same time is also beneficial for partial face matching. The approach requires neither learning procedures nor training data. Experiments are performed using the MBGC portal challenge data, comparing with several known state-of-the-arts methods. Extensive results show that the proposed approach, without knowing statistical characteristics of the subjects or data, outperforms the methods of contrast significantly, with ten-fold higher verification rates at FAR of 0.1%.
KeywordsMultiple biometric grand challenge (MBGC) MBGC portal challenge video based face recognition near infrared (NIR) heterogeneous face biometrics
- 1.NIST: Multiple Biometric Grand Challenge (MBGC) (2008), http://face.nist.gov/mbgc
- 2.Li, S.Z., Hou, X.W., Zhang, H.J.: Learning spatially localized, parts-based representation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Hawaii, December 11-13, vol. 1, pp. 207–212 (2001)Google Scholar
- 3.Martinez, A.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(6), 748–763 (2002)Google Scholar
- 4.Tarres, F., Rama, A.: A novel method for face recognition under partial occlusion or facial expression variations. In: 47th International Symposium on ELMAR, June 2005, pp. 163–166 (2005)Google Scholar
- 5.Bowyer, K.W., Chang, Flynn, P.J.: A survey of 3D and multi-modal 3D+2D face recognition. In: Proceedings of International Conference on Pattern Recognition, August 2004, pp. 358–361 (2004)Google Scholar
- 6.Kong, S.G., Heo, J., Abidi, B., Paik, J., Abidi, M.: Recent advances in visual and infrared face recognition - A review. Computer Vision and Image Understanding 97(1), 103–135 (2005)Google Scholar
- 7.Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (April 2007) (Special issue on Biometrics: Progress and Directions)Google Scholar
- 8.Tang, X., Wang, X.: Face sketch recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 50–57 (2004)Google Scholar
- 9.Lin, D., Tang, X.: Inter-modality face recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 13–26. Springer, Heidelberg (2006)Google Scholar
- 10.Yi, D., Liu, R., Chu, R., Lei, Z., Li, S.Z.: Face matching between near infrared and visible light images. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 523–530. Springer, Heidelberg (2007)Google Scholar
- 11.Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)Google Scholar