  It is well known that the performance of any classification model is
effective if the dataset used for the training process and the test process
satisfy some specific requirements. In other words, the more the dataset size
is large, balanced, and representative, the more one can trust the proposed
model's effectiveness and, consequently, the obtained results. Unfortunately,
large-size anonymous datasets are generally not publicly available in
biomedical applications, especially those dealing with pathological human face
images. This concern makes using deep-learning-based approaches challenging to
deploy and difficult to reproduce or verify some published results. In this
paper, we suggest an efficient method to generate a realistic anonymous
synthetic dataset of human faces with the attributes of acne disorders
corresponding to three levels of severity (i.e. Mild, Moderate and Severe).
Therefore, a specific hierarchy StyleGAN-based algorithm trained at distinct
levels is considered. To evaluate the performance of the proposed scheme, we
consider a CNN-based classification system, trained using the generated
synthetic acneic face images and tested using authentic face images.
Consequently, we show that an accuracy of 97,6\% is achieved using
InceptionResNetv2. As a result, this work allows the scientific community to
employ the generated synthetic dataset for any data processing application
without restrictions on legal or ethical concerns. Moreover, this approach can
also be extended to other applications requiring the generation of synthetic
medical images. We can make the code and the generated dataset accessible for
the scientific community.
