Improving Shadow Suppression for Illumination Robust Face Recognition

TL;DR

This paper introduces a novel illumination normalization method for face recognition that models reflectance and reconstructs shadow-free, color-preserving face images, significantly improving robustness against lighting variations.

Contribution

It presents a new reflectance-based approach that generates shadow-free face images while maintaining color and identity details, enhancing illumination robustness in face analysis.

Findings

Effective removal of soft and hard shadows in face images

Improved face recognition accuracy under varying lighting conditions

Robustness demonstrated across multiple face databases

Abstract

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.