We propose two face representations that are blind to facial expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which enforce data controllers to protect any kind of sensitive information involved in automatic processes. The advances in Affective Computing have contributed to improve human-machine interfaces but, at the same time, the capacity to monitorize emotional responses triggers potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these expression-blinded facial features. We show that it is possible to eliminate information related to emotion recognition tasks, while the performance of subject verification, gender recognition, and ethnicity classification are just slightly affected. We also present an application to train fairer classifiers in a case study of attractiveness classification with respect to a protected facial expression attribute. The results demonstrate that it is possible to reduce emotional information in the face representation while retaining competitive performance in other face-based artificial intelligence tasks.
翻译:我们建议两种面部表现方式与情感反应有关的面部表现方式不相容。这项工作的部分动机是新的个人数据保护国际条例,该条例要求数据控制员保护自动程序所涉及的任何敏感信息。女性计算机的进步有助于改进人体-机器界面,但与此同时,对情感反应进行监测的能力在公平和隐私两方面都对人类产生潜在风险。我们提出了两种不同的方法来学习这些表情盲面部特征。我们表明,有可能消除与情感识别任务有关的信息,而主题核查、性别识别和族裔分类的履行只是略为受到影响。我们还提出了一种应用,在对受保护面部表现属性的吸引力分类进行个案研究时,对更公平的分类人员进行培训。结果显示,在保持其他面部人工智能任务竞争性性的同时,可以减少面部情感表现方式。