An Efficient Pose & Illumination Normalization Preprocessing Technique for Face Recognition

Face recognition has made significant improvement in the last decade, but robust commercial applications are still lacking. Current authentication/ identification applications are limited to controlled settings, e.g., limited pose and illumination changes. This paper proposes a novel framework for real-world face recognition in uncontrolled settings named Face Analysis for Commercial Entities (FACE). Its robustness comes from normalization (“correction”) strategies to address pose and illumination variations. FACE adopts reliability indices, which estimate the “acceptability” of the final identification decision made by the classifier.    The rewards of FACE are: data fusion; online identity management; and interoperability. The results obtained by FACE witness a significant increase in accuracy when compared with the results produced by the other algorithmic rule considered.