This article presents an evolutionary approach for synthetic human portraits
generation based on the latent space exploration of a generative adversarial
network. The idea is to produce different human face images very similar to a
given target portrait. The approach applies StyleGAN2 for portrait generation
and FaceNet for face similarity evaluation. The evolutionary search is based on
exploring the real-coded latent space of StyleGAN2. The main results over both
synthetic and real images indicate that the proposed approach generates
accurate and diverse solutions, which represent realistic human portraits. The
proposed research can contribute to improving the security of face recognition
systems.

该文章使用基于潜在空间的演化方法生成综合人像，其中利用了 StyleGAN2 进行人像生成，FaceNet 对人脸相似度进行评估。经过对真实图像和合成图像的实验结果表明，该方法生成的解决方案既准确又多样化，代表着逼真的人类肖像，这一研究成果可为人脸识别系统的安全性提升做出贡献。

演化潜空间搜索的驱动人像生成

Evolutionary latent space search for driving human portrait generation

We propose Image-Semantic-Transformation-Reconstruction-Circle(ISTRC) model,
a novel and powerful method using facenet's Euclidean latent space to
understand the images. As the name suggests, ISTRC construct the circle, able
to perfectly reconstruct images. One powerful Euclidean latent space embedded
in ISTRC is FaceNet's last layer with the power of distinguishing and
understanding images. Our model will reconstruct the images and manipulate
Euclidean latent vectors to achieve semantic transformations and semantic
images arthimetic calculations. In this paper, we show that ISTRC performs 10
high-level semantic transformations like "Male and female","add smile","open
mouth", "deduct beard or add mustache", "bigger/smaller nose", "make older and
younger", "bigger lips", "bigger eyes", "bigger/smaller mouths" and "more
attractive". It just takes 3 hours(GTX 1080) to train the models of 10 semantic
transformations.

本文提出了一种名为 ISTRC 的模型，它使用 Facenet 的欧几里得潜空间来理解图像，能够实现完美重构，以实现语义变换和图像算术计算。ISTRC 可以执行包括男女性别、微笑、大嘴巴、减掉胡须或加上胡须、变大 / 变小的鼻子、老化及年轻化、变大的嘴唇、变大 / 变小的眼睛和嘴巴、更有吸引力等十种高级语义变换。训练模型只需要 3 小时。

图像语义转换：更快、更轻、更强

Image Semantic Transformation: Faster, Lighter and Stronger

Despite significant recent advances in the field of face recognition,
implementing face verification and recognition efficiently at scale presents
serious challenges to current approaches. In this paper we present a system,
called FaceNet, that directly learns a mapping from face images to a compact
Euclidean space where distances directly correspond to a measure of face
similarity. Once this space has been produced, tasks such as face recognition,
verification and clustering can be easily implemented using standard techniques
with FaceNet embeddings as feature vectors.
Our method uses a deep convolutional network trained to directly optimize the
embedding itself, rather than an intermediate bottleneck layer as in previous
deep learning approaches. To train, we use triplets of roughly aligned matching
/ non-matching face patches generated using a novel online triplet mining
method. The benefit of our approach is much greater representational
efficiency: we achieve state-of-the-art face recognition performance using only
128-bytes per face.
On the widely used Labeled Faces in the Wild (LFW) dataset, our system
achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves
95.12%. Our system cuts the error rate in comparison to the best published
result by 30% on both datasets.
We also introduce the concept of harmonic embeddings, and a harmonic triplet
loss, which describe different versions of face embeddings (produced by
different networks) that are compatible to each other and allow for direct
comparison between each other.

本文介绍了一种名为 FaceNet 的系统，其通过使用卷积神经网络及三元组学习法直接将人脸图像映射到紧凑的欧几里得空间中，使得人脸识别、验证和聚类等任务可使用 FaceNet 嵌入作为特征向量，仅使用 128 字节每个人脸即可实现最先进的人脸识别性能，其在 LFW 和 YouTube Faces DB 两个数据集上的表现都创造了最新纪录。同时，本文还提出了谐波嵌入的概念和谐波三元组损失，以描述不同网络产生的面部嵌入版本以及它们之间的直接比较。