SQI-BASED ILLUMINATION NORMALIZATION FOR FACE RECOGNITION BASED ON DISCRETE WAVELET TRANSFORM

Although self quotient image (SQI) has been popularly used for face recognition as a retinex-based based illumination normalization method, it suffers from strong shadows in faces with abrupt intensity change. In this paper, we propose SQI-based illumination normalization for face recognition based on discrete wavelet transform (DWT). To remove shadow edges while preserving features, we combine DWT with SQI into retinex-based illumination normalization. First, we divide a face image into four sub-bands using wavelet analysis to enhance vertical, horizontal, and diagonal edges. Then, we perform histogram truncation to remove very bright and dark regions. Finally, we conduct illumination normalization from SQI and the histogram truncation result based on the retinex theory. Experimental results demonstrate that the proposed method outperforms state-of-the-art retinex-based ones in terms of face recognition accuracy.