Most face detection and recognition tasks are based on the training of intact facial images and corresponding labels. Both the three‐dimensional (3D) structure and two‐dimensional (2D) appearance from the frontal view of human faces are bilaterally symmetrical in general. However, sometimes, illumination on the left and right halves of faces is uneven. In such cases, the symmetrical characteristic of human faces can facilitate expressing distinct identity information. This is because even if one side of the facial image is corrupted by noise, the opposite side can still be used for feature extraction. This paper proposes an automatic selection of the better half of the face using only a half‐face for identity recognition. Unlike the MegaFace challenge of recognizing millions of identities in the wild, this paper focuses on building recognition systems for a small number of people with fewer training images; the recognition system can, for example, build access control systems for research laboratory members or family members. This paper proposes an artificial face image construction method and a half‐face training strategy for transfer learning of pretrained conventional neural network models. Extensive experimental results show that the proposed method improves the performance of state‐of‐the‐art models by utilizing the symmetrical characteristics of human faces. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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面部对称特征在迁移学习中的应用

大多数面部检测和识别任务都是基于完整面部图像和相应标签的训练。从人脸正面看，三维（3D）结构和二维（2D）外观通常都是左右对称的。但是，有时候，左右两半的脸部照明不均匀。在这种情况下，人脸的对称特征可以促进表达不同的身份信息。这是因为即使面部图像的一侧被噪声破坏，另一侧仍然可以用于特征提取。本文提出了仅使用半脸来进行身份识别的自动选择脸的一半的方法。与MegaFace挑战不同，它需要在野外识别数百万个身份，本文着重于为少数人建立具有较少训练图像的识别系统。识别系统可以例如为研究实验室成员或家庭成员建立访问控制系统。本文提出了一种用于人工训练的常规神经网络模型的转移学习的人工人脸图像构建方法和半人脸训练策略。大量的实验结果表明，所提出的方法通过利用人脸的对称特性提高了最新模型的性能。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。本文提出了一种用于人工训练的常规神经网络模型的转移学习的人工人脸图像构建方法和半人脸训练策略。大量的实验结果表明，所提出的方法通过利用人脸的对称特性提高了最新模型的性能。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。本文提出了一种用于人工训练的常规神经网络模型的转移学习的人工人脸图像构建方法和半人脸训练策略。大量的实验结果表明，所提出的方法通过利用人脸的对称特性提高了最新模型的性能。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。