Low cost illumination invariant face recognition by down-up sampling self quotient image

Illumination variation generally causes performance degradation of face recognition systems under real-life environments. The Self Quotient Image (SQI) method [1] is proposed to remove extrinsic lighting effects but requires high computation complexity. Therefore, we propose a low cost face recognition scheme that uses multi-scale down-up sampling to generate self quotient image (DUSSQI) to remove the lighting effects. The DUSSQI has the following advantages: (1) Remove the lighting artifacts effectively. (2) Extract different face details including texture and edges. (3) Only global operation on pixels is required to reduce computational cost. Experimental results demonstrate that our proposed approach achieves 98.58% recognition rate for extended YaleB database and 93.8% for FERET database under various lighting conditions and reduces 97.1% computational time compared to that of SQI.