Multi-Task Faces (MTF) dataset

Human facial data hold tremendous potential to address a variety of classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. However, recent privacy regulations, such as the EU General Data Protection Regulation, have restricted the ways in which human images may be collected and used for research. As a result, several previously published data sets containing human faces have been removed from the internet due to inadequate data collection methods that failed to meet privacy regulations. Data sets consisting of synthetic data have been proposed as an alternative, but they fall short of accurately representing the real data distribution. On the other hand, most available data sets are labeled for just a single task, which limits their applicability. To address these issues, we present a collection of face images designed for various classification tasks, including face recognition and classification by race, gender, and age, as well as aiding to train generative networks. We named this collection the Multi-Task Face (MTF) data, and it is provided in two flavors: a non-curated data set that includes 132,816 images of 640 individuals, and a manually curated version with 5,246 images of 240 individuals meticulously selected to maximize their classification quality. The MTF data sets have been ethically gathered by leveraging publicly available images of celebrities and strictly adhering to copyright regulations. In addition to presenting the data and providing detailed descriptions of the collection and processing procedures followed, we also evaluate the suitability of the data for training five deep learning (DL) models across the aforementioned classification tasks.

人脸数据蕴含着解决多种分类问题，诸如人脸识别、年龄估算、性别识别、情感分析和种族分类等，的巨大潜力。然而，近期实施的隐私法规，如欧盟通用数据保护条例等，对人类图像的收集和使用方式进行了限制。因此，由于数据收集方法未能满足隐私法规要求，一些先前发布包含人脸数据的数据集已被从互联网上移除。作为替代方案，提出了由合成数据组成的数据集，但它们在准确反映真实数据分布方面存在不足。另一方面，大多数现有的数据集仅针对单一任务进行标注，这限制了其适用性。为解决这些问题，我们呈现了一组旨在应对多种分类任务的人脸图像，包括人脸识别、按种族、性别和年龄进行的分类，以及辅助训练生成网络。我们将此集合命名为多任务人脸（MTF）数据集，并提供了两种版本：一种非精选数据集，包含640位个体的132,816张图像，以及一种经过人工精选的版本，包含240位个体的5,246张图像，旨在最大化其分类质量。MTF数据集通过利用公众可获取的明星图像并严格遵守版权法规的方式，进行了道德性的收集。此外，我们不仅展示了数据，还提供了关于收集和处理过程的详细描述，并对数据在训练五个深度学习（DL）模型以执行上述分类任务方面的适用性进行了评估。