We present a method for estimating detailed scene illumination using human
faces in a single image. In contrast to previous works that estimate lighting
in terms of low-order basis functions or distant point lights, our technique
estimates illumination at a higher precision in the form of a non-parametric
environment map. Based on the observation that faces can exhibit strong
highlight reflections from a broad range of lighting directions, we propose a
deep neural network for extracting highlights from faces, and then trace these
reflections back to the scene to acquire the environment map. Since real
training data for highlight extraction is very limited, we introduce an
unsupervised scheme for finetuning the network on real images, based on the
consistent diffuse chromaticity of a given face seen in multiple real images.
In tracing the estimated highlights to the environment, we reduce the blurring
effect of skin reflectance on reflected light through a deconvolution
determined by prior knowledge on face material properties. Comparisons to
previous techniques for highlight extraction and illumination estimation show
the state-of-the-art performance of this approach on a variety of indoor and
outdoor scenes.

本文提出了一种利用单张图片中的人脸来估计场景光照的方法，通过深度神经网络提取高光并追溯回环境中，得到非参数化的环境图，该方法表现出了最新技术水平，适用于各种室内外场景。

利用无监督深度高光提取技术将人脸作为光探针

Faces as Lighting Probes via Unsupervised Deep Highlight Extraction

In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN.

利用卷积神经网络对场景照明进行精确预测，在空间域中以图像块为输入，不使用手工制作的特征，该网络包括一个卷积层、一个最大池化层、一个全连接层和三个输出节点，将特征学习和回归一体化到一个优化过程中，在常规的 RAW 图像数据集上取得了最新成果，对具有空间变化照明的图像的初步实验表明 CNN 局部光源估计能力的稳定性。