To address the issue that 2D face recognition methods are susceptible to external environmental interference,a deep learning-based 3D face recognition method is proposed.The method extracts features from face geometric information and demonstrates strong robustness to environmental factors such as lighting.According to the analysis of existing research,a dual-domain feature enhancement module is designed.This module extracts local facial features from both the channel domain and the spatial domain,and uses them as enhancements to the global features,resulting in more comprehensive facial features.Additionally,a novel feature learning strategy tailored for 3D face recognition is proposed to address the characteristics of 3D face data.This strategy aims to enable face recognition models to extract identity features from the depth relationships of 3D faces and it can significantly alleviate the negative impact of noise in 3D faces on feature computation.On the public datasets Bosphorus and Texas,verification accuracies of 96.32%and 98.93%are achieved,respectively.The results demonstrate that the proposed method can achieve higher recognition accuracy and also has certain advantages in the face recognition under complex conditions.
关键词
3D人脸识别/深度学习/深度关系感知/双域特征增强
Key words
3D face recognition/deep learning/deep relationship perception/dual domain feature enhancement
维普
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