REFLECTANCE PERCEPTION MODEL BASED FACE RECOGNITION IN DISSIMILAR ILLUMINATION CONDITIONS
Abstract
Reflectance Perception Based Face Recognition in different Illuminating Conditions is presented. Face recognition algorithms have to deal with significant amounts of illumination variations between gallery and probe images. Many of the State-of-the art commercial face recognition algorithms still struggle with this problem. In this projected work a new algorithm is stated for the preprocessing method which compensated for illumination variations in images along with a robust Principle Component Analysis (PCA) based Facial Feature Extraction is stated which is used to improve and reduce the dimension of the image by removing the unwanted vectors by the weighted Eigen faces. The proposed work demonstrates large performance improvements with several standard face recognition algorithms across multiple, publicly available face databases.
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