Improving Shadow Suppression for Illumination Robust Face Recognition
This addresses the challenge of reliable face analysis under uncontrolled lighting for applications like security and verification, representing an incremental improvement over existing methods.
The paper tackles the problem of illumination sensitivity in face recognition by proposing a reflectance model-based preprocessing method that generates a Chromaticity Intrinsic Image to suppress shadows while preserving color and identity details, with experimental results demonstrating effectiveness across various scenarios and databases.
2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. An illumination robust preprocessing method thus remains a significant challenge in reliable face analysis. In this paper we propose a novel approach for improving lighting normalization through building the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborates the formation of face color appearance. Specifically, the proposed illumination processing pipeline enables the generation of Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach to deal with lighting variations, including both soft and hard shadows, in face recognition.