SeLENet:一种用于移动人脸解锁的半监督低光人脸增强方法

Ha A. Le, I. Kakadiaris
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引用次数: 5

摘要

面部识别正在成为新型智能手机的标准功能。然而,使用常规2D相机传感器的设备的面部解锁功能在弱光环境下表现不佳。本文提出了一种半监督弱光人脸增强方法,以提高弱光人脸图像的人脸验证性能。该方法是一个由分解和重构两部分组成的网络。分解分量将输入的弱光人脸图像分解为人脸法线和人脸反照率,重构分量利用环境白光直射的球谐光照系数增强和重构输入图像的光照条件。该网络以半监督的方式使用标记的合成数据和未标记的真实数据进行训练。定性结果表明,该方法比目前最先进的弱光增强算法产生更真实的图像。定量实验验证了该方法在人脸验证中的有效性。应用该方法,将极弱光人脸图像与中性光人脸图像的验证精度差距从约3%减小到0.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SeLENet: A Semi-Supervised Low Light Face Enhancement Method for Mobile Face Unlock
Facial recognition is becoming a standard feature on new smartphones. However, the face unlocking feature of devices using regular 2D camera sensors exhibits poor performance in low light environments. In this paper, we propose a semi-supervised low light face enhancement method to improve face verification performance on low light face images. The proposed method is a network with two components: decomposition and reconstruction. The decomposition component splits an input low light face image into face normals and face albedo, while the reconstruction component enhances and reconstructs the lighting condition of the input image using the spherical harmonic lighting coefficients of a direct ambient white light. The network is trained in a semi-supervised manner using both labeled synthetic data and unlabeled real data. Qualitative results demonstrate that the proposed method produces more realistic images than the state-of-the-art low light enhancement algorithms. Quantitative experiments confirm the effectiveness of our low light face enhancement method for face verification. By applying the proposed method, the gap of verification accuracy between extreme low light and neutral light face images is reduced from approximately 3% to 0.5%.
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