Tensor-based subspace learning and its applications in multi-pose face synthesis
Tensor-based subspace learning and its applications in multi-pose face synthesis
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DOI：
10.1016/j.neucom.2010.04.013
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发表时间：
2010-08
期刊：
影响因子：
6
通讯作者：
Xu Qiao;X. Han;T. Igarashi;K. Nakao;Yenwei Chen
中科院分区：
文献类型：
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作者：
Xu Qiao;X. Han;T. Igarashi;K. Nakao;Yenwei Chen
人脸姿态合成在公共安全、面部美容等领域有着广泛的应用，如何在没有空间信息的情况下从一幅图像中准确地合成人脸姿态图像仍然是一个具有挑战性的问题。在本文中，我们提出了一个基于张量的子空间学习方法（TSL）的合成人类多姿态的人脸图像从一个单一的二维图像。在所提出的TSL方法中，数据库中的二维多姿态图像被预先组织成张量形式，并应用张量分解技术来构建投影子空间。在合成过程中，首先将输入的二维图像投影到其对应的投影子空间中得到一个单位向量，然后利用单位向量生成其他新的姿态图像。我们的技术应用于KAO-Ritsumeikan多角度视图，照明和化妆品人脸数据库（MaVIC）和实验结果表明，我们提出的方法的有效性，面部姿态合成。
Facial pose synthesis is applied to generate much required information for several applications, such as public security, facial cosmetology, etc. How to synthesize facial pose images from one image accurately without spatial information is still a challenging problem. In this paper we propose a tensor-based subspace learning method (TSL) for synthesizing human multi-pose facial images from a single two-dimensional image. In the proposed TSL method, two-dimensional multi-pose images in the database are previously organized into a tensor form and a tensor decomposition technique is applied to build projection subspaces. In synthesis processing, the input two-dimensional image is first projected into its corresponding projection subspace to get an identity vector and then the identity vector is used to generate other novel pose images. Our technique is applied on KAO-Ritsumeikan Multi-angle View, Illumination and Cosmetic Facial Database (MaVIC) and experiment results show the effectiveness of our proposed method for facial pose synthesis.