Yao Yajun, Wang Chongwen
Zhengzhou Power Supply Company, State Grid Henan Electric Power Company, Zhengzhou, 450000, China.
School of Computer Science, Beijing Institute of Technology, Beijing, 100000, China.
Sci Rep. 2024 Nov 18;14(1):28419. doi: 10.1038/s41598-024-80032-x.
In recent years, face recognition technology has made significant progress in the field of real visual images, yet face recognition involving caricature-visual images remains a challenge due to the exaggerated and unrealistic features of caricature faces. To tackle this issue, this paper introduces the Caricature-visual Face Recognition Model Based on Jigsaw Solving and Modal Decoupling (CVF-JSM). The CVF-JSM consists of two modules: feature extraction and decoupling. The feature extraction module incorporates a graph attention network at the intermediate stage of the backbone network, which constructs and solves jigsaw puzzles to enable the network to extract shape features. The feature decoupling module features a three-branch structure that divides the features into modal and identity features. The real and caricature face recognition branches separate identity features for recognition through parameter sharing and orthogonality constraints. The feature common subspace alignment branch maps the anchor image, as well as the positive and negative sample images, into a common subspace to isolate identity features. Subsequently, by aligning the features, it further refines the effective identity features. The experimental results conducted on multiple datasets demonstrate that the CVF-JSM model outperforms existing technologies in the realm of caricature-visual face recognition.
近年来，人脸识别技术在真实视觉图像领域取得了重大进展，然而，由于漫画人脸具有夸张和不现实的特征，涉及漫画视觉图像的人脸识别仍然是一个挑战。为了解决这个问题，本文介绍了基于拼图求解和模态解耦的漫画视觉人脸识别模型（CVF-JSM）。CVF-JSM由两个模块组成：特征提取和解耦。特征提取模块在主干网络的中间阶段引入了一个图注意力网络，该网络构建并解决拼图问题，以使网络能够提取形状特征。特征解耦模块具有一个三分支结构，将特征分为模态特征和身份特征。真实和漫画人脸识别分支通过参数共享和正交约束分离身份特征以进行识别。特征公共子空间对齐分支将锚定图像以及正、负样本图像映射到一个公共子空间中，以分离身份特征。随后，通过对齐特征，进一步细化有效的身份特征。在多个数据集上进行的实验结果表明，CVF-JSM模型在漫画视觉人脸识别领域优于现有技术。
