Recent progress in deep learning has been accompanied by a growing concern for whether models are fair for users, with equally good performance across different demographics. In computer vision research, such questions are relevant to face detection and the related task of face attribute detection, among others. We measure race and gender inclusion in the context of smiling detection, and introduce a method for improving smiling detection across demographic groups. Our method introduces several modifications over existing detection methods, leveraging twofold transfer learning to better model facial diversity. Results show that this technique improves accuracy against strong baselines for most demographic groups as well as overall. Our best-performing model defines a new state-of-the-art for smiling detection, reaching 91% on the Faces of the World dataset. The accompanying multi-head diversity classifier also defines a new state-of-the-art for gender classification, reaching 93.87% on the Faces of the World dataset. This research demonstrates the utility of modeling race and gender to improve a face attribute detection task, using a twofold transfer learning framework that allows for privacy towards individuals in a target dataset.

本研究提出了一种面部属性检测的方法，即在学习属性检测任务之前学习人口统计信息，从而在性别和种族子群中保持或提高属性检测的准确性。该系统名为InclusiveFaceNet，通过转移从公开种族和性别身份的数据集中学习到的种族和性别表示来检测面部属性，同时在保留潜在用户人口统计隐私的同时，获得了迄今为止面部属性检测中关于Faces of the World和CelebA数据集中最佳的报告数字之一。

InclusiveFaceNet: 利用种族与性别多样性提升人脸属性检测