Face Recognition Under Varying Illumination Using Gradientfaces

• Published in: IEEE transactions on image processing Vol. 18; no. 11; pp. 2599 - 2606
• Main Authors: Zhang, Taiping, Tang, Yuan Yan, Fang, Bin, Shang, Zhaowei, Liu, Xiaoyu
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Databases, Factual; Degradation; Exact sciences and technology; Face - anatomy & histology; Face recognition; Facial; Feature extraction; gradient domain; gradientfaces; Humans; Illumination; illumination insensitive measure; Image databases; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Information, signal and communications theory; Lighting; Natural lighting; Normal Distribution; Outdoor; Pattern recognition; Pattern Recognition, Automated - methods; Pixel; Pixels; Power measurement; Robustness; Signal processing; Studies; Telecommunications and information theory; Biometrics; gradient domain; Face recognition; Image processing; Outdoor installation; Database; Artefact; Pattern recognition; gradientfaces; Automatic recognition; illumination insensitive measure; Facial expression
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2009.2028255

In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the gradient faces. Theoretical analysis shows gradient faces is an illumination insensitive measure, and robust to different illumination, including uncontrolled, natural lighting. In addition, gradient faces is derived from the image gradient domain such that it can discover underlying inherent structure of face images since the gradient domain explicitly considers the relationships between neighboring pixel points. Therefore, gradient faces has more discriminating power than the illumination insensitive measure extracted from the pixel domain. Recognition rates of 99.83% achieved on PIE database of 68 subjects, 98.96% achieved on Yale B of ten subjects, and 95.61% achieved on Outdoor database of 132 subjects under uncontrolled natural lighting conditions show that gradient faces is an effective method for face recognition under varying illumination. Furthermore, the experimental results on Yale database validate that gradient faces is also insensitive to image noise and object artifacts (such as facial expressions).