Abstract: In this paper we investigate how to perform face recognition on the hardest experiment (Exp4) in Face Recognition Grand Challenge(FRGC) phase-II ...
We use Support Vector Machines for fusing these two to boost the performance to [email protected] % FAR on the first large-scale face database such as the FRGC dataset.
It is empirically found that the eye-region is the most discriminative feature of the faces in FRGC data and yields a verification rate closest to the ...
PDF | In this paper we investigate how to perform face recognition on the hardest experiment (Exp4) in Face Recognition Grand Challenge(FRGC) phase-II.
We use Support Vector Machines for fusing these two to boost the performance to [email protected] % FAR on the first large-scale face database such as the FRGC dataset.
Partial & Holistic Face Recognition on FRGC-II data using Support Vector Machine. Savvides M., Abiantun R., Heo J., Park S., Xie C., Vijayakumar B.V.. Expand.
Partial & holistic face recognition on frgc-ii data using support vector machine. M Savvides, R Abiantun, J Heo, S Park, C Xie, BVK Vijayakumar. 2006 ...
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Xie, C. Vijayakumar, B.V.K.. Partial & Holistic Face Recognition on FRGC-II data using Support Vector Machine Kernel Correlation Feature Analysis IEEE CVPR 2006
Partial & Holistic Face Recognition on FRGC-II data using Support Vector. Machine Kernel Correlation Feature Analysis. Marios Savvides, Ramzi Abiantun, Jingu ...
Xie, B.V. Kumar, Partial and holistic face recognition on frgc-ii data using support vector machines, in: Proc. of IEEE ICCVPR, Vol. 48, 2006 ...