Background Learning for Robust Face Recognition

In this paper, we propose a robust face recognition technique based on the principle of eigenfaces. The traditional eigenface recognition (EFR) method works quite well when the input test patterns are cropped faces. However, when confronted with recognizing faces embedded in arbitrary backgrounds, the EFR method fails to discriminate effectively between faces and background patterns, giving rise to many false alarms. In order to improve robustness in the presence of background, we argue in favor of learning the distribution of background patterns. A background space is constructed from the background patterns and this space together with the face space is used for recognizing faces. The proposed method outperforms the traditional EFR technique and gives very good results even on complicated scenes.