In biomedical research and artificial intelligence, access to large,
well-balanced, and representative datasets is crucial for developing
trustworthy applications that can be used in real-world scenarios. However,
obtaining such datasets can be challenging, as they are often restricted to
hospitals and specialized facilities. To address this issue, the study proposes
to generate highly realistic synthetic faces exhibiting drug abuse traits
through augmentation. The proposed method, called "3DG-GA", Deep De-identified
anonymous Dataset Generation, uses Genetics Algorithm as a strategy for
synthetic faces generation. The algorithm includes GAN artificial face
generation, forgery detection, and face recognition. Initially, a dataset of
120 images of actual facial drug abuse is used. By preserving, the drug traits,
the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse
images. The dataset will be open to the scientific community, which can
reproduce our results and benefit from the generated datasets while avoiding
legal or ethical restrictions.

通过遗传算法，以生物医学研究和人工智能为基础，为科学界提供可复现的数据集的研究中，通过脸部特征增强生成高度逼真的合成面部器官，在合成过程中保留药物滥用特征的方法，使用数据集包含 120 张实际面部药品滥用的图片，最终可以提供 3000 张合成面部药品滥用图像的数据集，绕过法律和道德限制，受到广泛的关注和可以应用于实际场景的信任。

使用遗传算法通过脱敏匿名数据集增强生成毒品滥用的人脸图像 (3DG-GA)

Generation of artificial facial drug abuse images using Deep  De-identified anonymous Dataset augmentation through Genetics Algorithm  (3DG-GA)

We present a minimalistic but effective neural network that computes dense
facial correspondences in highly unconstrained RGB images. Our network learns a
per-pixel flow and a matchability mask between 2D input photographs of a person
and the projection of a textured 3D face model. To train such a network, we
generate a massive dataset of synthetic faces with dense labels using
renderings of a morphable face model with variations in pose, expressions,
lighting, and occlusions. We found that a training refinement using real
photographs is required to drastically improve the ability to handle real
images. When combined with a facial detection and 3D face fitting step, we show
that our approach outperforms the state-of-the-art face alignment methods in
terms of accuracy and speed. By directly estimating dense correspondences, we
do not rely on the full visibility of sparse facial landmarks and are not
limited to the model space of regression-based approaches. We also assess our
method on video frames and demonstrate successful per-frame processing under
extreme pose variations, occlusions, and lighting conditions. Compared to
existing 3D facial tracking techniques, our fitting does not rely on previous
frames or frontal facial initialization and is robust to imperfect face
detections.

本文提出了一种使用神经网络在高度自由的 RGB 图像中计算面部对应关系的最简单有效的方法，使用渲染可变姿势、表情、照明和遮挡的多样化三维面部模型生成大量人工数据集，结合真实照片培训表现在精度和速度上优于现有方法，并可以成功地处理极端姿势变化、遮挡和光照条件。