Multimodal Face Pair Matching System

Ms. Divya K. Sawant, Prof. N. M. Shahane


A face recognition system can be thought as an
identification or verification system. Face pair matching is a
challenging task which aims to determine whether two face
images represent the same person. Many times the face images
being compared are having complicated facial variations and
limited expressive information which becomes a difficult problem.
To address these issues, the proposed approach concentrate on
exploiting an additional set of face images called as cohort set.
The proposed system is able to perform multimodal face pair
matching using cohort information. The inputs to this system
are pair of face images to be matched and the set of cohort face
images. All cohort images are ranked separately based on pair
of face images to generate two lists. Then cohort information are
extracted from two sorted cohort list and combined with direct
matching score of the two input face images to form modality.
The final decision of matching is made by fusing all available
face modalities.

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G. B. Huang, M. Mattar, T. Berg, and E. L. Miller, Labeled faces in

the wild: A database for studying face recognition in unconstrained

environments, Univ. Massachusetts, Amherst, MA, USA, Tech. Rep. 07-

, 2008, vol. 1, no. 2.

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, Face recognition:

A literature survey, ACM Comput. Surv., vol. 35, no. 4, pp. 399458, 2003.

Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, A survey of affect

recognition methods: Audio, visual, and spontaneous expressions, IEEE

Trans. Pattern Anal. Mach. Intell., vol. 31, no. 1, pp. 3958, Jan. 2009.

H. Han, C. Otto, X. Liu, and A. K. Jain, Demographic estimation from

face images: Human vs. machine performance, IEEE Trans. Pattern Anal.

Mach. Intell., vol. 37, no. 6, pp. 11481161, Jun. 2015.

X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang, Face recognition from a

single image per person: A survey, Pattern Recognit., vol. 39, no. 9, pp.

, 2006.

F. Schroff, T. Treibitz, D. Kriegman, and S. Belongie, Pose, illumination

and expression invariant pairwise face-similarity measure via Doppelgnger list comparison, in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011,

pp. 24942501

Q. Yin, X. Tang, and J. Sun, An associate-predict model for face

recognition, in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit.,

Jun. 2011, pp. 497504.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, Robust face

recognition via sparse representation, IEEE Trans. Pattern Anal. Mach.

Intell., vol. 31, no. 2, pp. 210227, Feb. 2009.

W. Deng, J. Hu, and J. Guo, Extended SRC: Undersampled face recognition via intraclass variant dictionary, IEEE Trans. Pattern Anal. Mach.

Intell., vol. 34, no. 9, pp. 18641870, Sep. 2012.

M. Yang, L. Van Gool, and L. Zhang, Sparse variation dictionary

learning for face recognition with a single training sample per person,

in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 689696.

S. Liao, A. K. Jain, and S. Z. Li, Partial face recognition: Alignmentfree

approach, IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 5, pp.

, May 2013.

M. Tistarelli, Y. Sun, and N. Poh, On the use of discriminative cohort

score normalization for unconstrained face recognition, IEEE Trans. Inf.

Forensics Security, vol. 9, no. 12, pp. 20632075, Dec. 2014.

L. Wolf, T. Hassner, and Y. Taigman, Effective unconstrained face

recognition by combining multiple descriptors and learned background

statistics, IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 10, pp.

, Oct. 2011.

H. Li, G. Hua, Z. Lin, J. Brandt, and J. Yang, Probabilistic elastic

matching for pose variant face verification, in Proc. IEEE Int. Conf.

Comput. Vis. Pattern Recognit., Jun. 2013, pp. 34993506.

K. Simonyan, O. M. Parkhi, A. Vedaldi, and A. Zisserman, Fisher vector

faces in the wild, in Proc. Brit. Mach. Vis. Conf., 2013, pp. 112


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