  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 and
others, 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 the Multi-Task Faces
(MTF) image data set, a meticulously curated collection of face images designed
for various classification tasks, including face recognition, as well as race,
gender, and age classification. The MTF data set has been ethically gathered by
leveraging publicly available images of celebrities and strictly adhering to
copyright regulations. In this paper, we present this data set and provide
detailed descriptions of the followed data collection and processing
procedures. Furthermore, we evaluate the performance of five deep learning (DL)
models on the MTF data set across the aforementioned classification tasks.
Additionally, we compare the performance of DL models over the processed MTF
data and over raw data crawled from the internet. The reported results
constitute a baseline for further research employing these data. The MTF data
set can be accessed through the following link (please cite the present paper
if you use the data set): https://github.com/RamiHaf/MTF_data_set
