Face recognition for target detection on PCA features with outlier information

This work addresses face recognition for detecting a small and particular set of individuals over a huge population of people. A new approach is developed which uses training samples of both target classes and non-target outliers. Two-stage Principal Component Analysis (PCA) schemes are proposed for flexible feature extraction. Experimental results reveal that the new approach improves the accurate rate of detection significantly.