“Curse of Mis-Alignment” Problem in Face Recognition
Shiguang Shan, J Isvision · Chinese Journal of Computers · 2005
In this paper, authors investigate the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel perturbation learning solution. Mis-alignment problem is firstly empirically investigated through evaluating systematically the Fisherface’s sensitivity to mis-alignment on the FERET face database by perturbing the eye coordinates, which reveals that the imprecise localization of the facial landmarks abruptly degrades the Fisherface system. Authors explicitly define this problem as curse of mis-alignment for highlighting its significance. Aiming at this problem, authors propose a set of measurement combining the recognition rate with the alignment error distribution to evaluate the overall performance of specific face recognition approach with its robustness against the mis-alignment considered. Finally, a perturbation learning method,named E-Fisherface, is proposed to reinforce the recognizer to model the mis-alignment variations in the training stage.Experimental results on FERET and CAS-PEAL-R1 have impressively indicated the effectiveness of the proposed E-Fisherface to tackle the curse of mis-alignment problem.