Latent space visualization of half face and full face by generative model
Latent space visualization of half face and full face by generative model
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DOI：
10.1117/12.2588980
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Zou Min;T. Akashi
中科院分区：
文献类型：
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作者：
Zou Min;T. Akashi
通常，大多数人脸检测和识别任务都是基于完整的人脸图像及其相应的标签的训练。训练图像应该包含尽可能多的面部区域，有时将训练图像区域扩展到上半身也可以增强学习能力。然而，我们注意到人脸的三维结构和从正面看的二维外观都是两侧对称的。很少有研究利用这一特点来简化学习过程。我们已经提出了一种翻转策略，将面部对称特性应用于迁移学习，并证明了半张脸的训练也可以在一小群人的面部识别中实现同等的性能。本文扩展了迁移学习的裁剪半人脸图像的人脸识别，而不是翻转的半脸。利用人脸的对称性，通过对一半的普通人脸图像进行迁移学习，提高人脸识别率。我们还调查和解释了为什么半脸面积足以准确地分类小群体的个人。变分自动编码器网络被用来施加的概率分布上的面部潜在空间。最后，减少人脸潜在空间的维度，以可视化人脸身份的分布式感知流形。
Generally, most face detection and recognition tasks are based on the training of intact facial images and their corresponding labels. The training image is supposed to contain as much facial area as possible, and sometimes expanding the training image area to the upper body may also enhance the learning ability. However, we noticed that both the three-dimensional structure and two-dimensional appearance from the frontal view of human faces are bilaterally symmetrical. Few research makes use of this characteristics to simplify the learning process. We have proposed a flipping strategy to apply the facial symmetrical characteristic to transfer learning and proved training with half faces can also achieve equivalent performance in face recognition for a small group of individuals. This paper extend the transfer learning of cropped half face images for face recognition rather than flipping the half face. The facial symmetrical characteristics is utilized to improve face recognition through transfer learning of only a half of the common human face image. We also investigate and explain the reason why the half face area is enough to accurately classify small groups of individuals. A variational autoencoder network is utilized to impose the probability distribution on the facial latent space. Finally, the dimensions of the facial latent space are reduced to visualize the distributed perceptual manifold for face identity.