期刊
PATTERN RECOGNITION
卷 134, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2022.109065
关键词
Deep 3D face recognition; Point clouds; Adversarial samples; Meta -learning
Recently, deep face recognition using 2D face images has advanced due to the availability of large-scale face data. However, deep face recognition using 3D face scans on point clouds still needs further exploration. This paper proposes a meta-learning-based adversarial training algorithm for deep 3D face recognition on point clouds. The algorithm combines adversarial sample generation and meta-learning-based network training to continuously generate diverse adversarial samples and improve the accuracy of the 3DFR model.
Recently, deep face recognition using 2D face images has made great advances mainly due to the readily available large-scale face data. However, deep face recognition using 3D face scans, especially on point clouds, has been far from fully explored. In this paper, we propose a novel meta-learning-based adversarial training (MLAT) algorithm for deep 3D face recognition (3DFR) on point clouds. It consists of two alternate modules: adversarial sample generating for 3D face data augmentation and meta-learning-based deep network training. In the first module, adversarial samples of given 3D face scans are dynamically generated based on current deep 3DFR model. In the second module, a meta-learning framework is designed to avoid the performance decrease caused by the generated adversarial samples. Overall, MLAT algorithm combines the adversarial sample generating and meta-learning-based network training in a uniform framework, in which adversarial samples and network parameters are optimized alternately. Thus, it can continuously generate diverse and suitable adversarial samples, and then the meta-learning framework can further improve the accuracy of 3DFR model. Comprehensive experimental results show that the proposed approach consistently achieves competitive rank-one recognition accuracies on the BU-3DFE (10 0%), Bosphorus (99.78%), BU-4DFE (98.02%) and FRGC v2 (98.01%) database, and thereby substantiate its superiority.
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