Illumination invariant feature based on neighboring radiance ratio
Xi Zhang, Xiaolin Wu · 2017
In many object recognition applications, especially in face recognition, varying illuminations can adversely affect the robustness of the object recognition system. In this paper, we propose a novel illumination invariant feature called Neighboring Radiance Ratio (NRR) which is insensitive to both intensity and direction of light. NRR is derived and analyzed based on a physical image formation model. The computation of NRR does not need any prior information or any training data and NRR is far less sensitive to the border of shadows than most existing methods. The analysis of the illumination invariance of NRR is also presented. The proposed NRR feature is tested on Extended Yale B and CMU-PIE databases and compared with several previous methods. The experimental results corroborate our analysis and demonstrate that NRR is highly robust image feature against illumination changes.