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. The current massive data-driven approach, e.g., deep learning-based face recognition, requires a huge amount of labeled training face data that hardly cover the infinite lighting variations that can be encountered in real-life applications. An illumination robust preprocessing method thus remains a very interesting but also a significant challenge in reliable face analysis. In this paper we propose a novel model driven approach to improve lighting normalization of face images. Specifically, we propose to build the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborate the formation of face color appearance. The proposed illumination processing pipeline enables generation of the 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 in dealing with lighting variations, including both soft and hard shadows, in face recognition.

中文翻译：

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改善照明鲁棒人脸识别的阴影抑制


2D 人脸分析技术，例如人脸标记、人脸识别和人脸验证，相当依赖于现实世界中通常不受控制和不可预测的照明条件。当前的海量数据驱动方法，例如基于深度学习的人脸识别，需要大量标记的训练人脸数据，而这些数据很难覆盖现实生活应用中可能遇到的无限光照变化。因此，光照稳健的预处理方法仍然是一个非常有趣的方法，但也是可靠的人脸分析中的一个重大挑战。在本文中，我们提出了一种新颖的模型驱动方法来改进面部图像的光照归一化。具体来说，我们建议建立底层反射模型来表征皮肤表面、光源和相机传感器之间的相互作用，并详细说明面部颜色外观的形成。所提出的照明处理管道能够在对数色度空间中生成色度本征图像（CII），该空间对照明变化具有鲁棒性。此外，作为与大多数流行方法相比的一个优点，随后重建了逼真的彩色面部图像，这消除了各种阴影，同时保留了颜色信息和身份细节。不同场景下和使用各种人脸数据库的实验结果表明，所提出的方法在处理人脸识别中的光照变化（包括软阴影和硬阴影）方面的有效性。