Imprecise alignment (e.g. eye centers) will greatly degrade the face recognition performance, which is explicitly defined by us as curse of mis-alignment. we have investigated this problem and proposed an enhance FDA solution. 

• Curse of Mis-alignment in Face Recognition

We present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Mis-alignment problem is firstly empirically investigated through systematically evaluating 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 degenerates the Fisherface system. We explicitly define this problem as curse of mis-alignment to highlight its graveness. We then analyze the sources of curse of mis-alignment and group the possible solutions into three categories: invariant features, mis-alignment modeling, and alignment retuning. And then we 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 novel mis-alignment learning method, named E-Fisherface, is proposed to reinforce the recognizer to model the mis-alignment variations. Experimental results have impressively indicated the effectiveness of the proposed E-Fisherface in tackling the curse of mis-alignment problem. Please refer to our FG2004 paper for more information or contact Dr.Shiguang Shan.

• Review the Strength of Gabor features for Face Recognition from the Angle of its Robustness to Mis-alignment

Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation. Please refer to our ICPR2004 paper for more information or contact Dr.Shiguang Shan.