التفاصيل البيبلوغرافية
| العنوان: |
基于内容风格增强和特征嵌入优化的人脸活体检测方法. (Chinese) |
| العنوان البديل: |
Face anti-spoofing method based on content style enhancement and feature embedding optimization. (English) |
| المؤلفون: |
何东, 郭辉, 李振东, 刘昊 |
| المصدر: |
Application Research of Computers / Jisuanji Yingyong Yanjiu; Jun2024, Vol. 41 Issue 6, p1869-1875, 7p |
| الملخص (بالإنجليزية): |
In response to the issues of inadequate feature representation in existing face anti-spoofing algorithms and poor cross-dataset generalization performance, this paper proposed a face anti-spoofing method based on content-style enhancement and feature embedding optimization. Firstly, this method utilized a ResNet-18 encoder to extract generic features from multiple source domains, and then subjected to separation through two self-adaptive modules with different attention mechanisms, enhancing the representation of global content features and local style features. Secondly, based on the AdaIN algorithm, it organically fused content features with style features, further improving the feature representation, and the fused features were subsequently input to specific classifiers and domain discriminators for adversarial training. Finally, by employing average negative samples and semi-hard sample triplet mining to optimize feature embeddings, effectively striking a balance between intra-class cohesion and inter-class discrimination, better capturing the boundaries between genuine and spoofed categories. The proposed method was trained and tested on four benchmark datasets, suchas CASIA-FASD, REPLAY-ATTACK, MSU-MFSD, and OULU-NPU. It achieves accuracy of 6.33%, 12.05%, 8.38% and 10.59% respectively, which are superior to existing algorithms. This indicates that the proposed method can significantly improve the generalization performance of face live detection models in cross-dataset testing. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
针对现有人脸活体检测算法的特征表示不佳，以及在跨数据集上泛化性能较差等问题，提出了一种基于内容风格增强和特征嵌入优化的人脸活体检测方法。首先，使用ResNet-18编码器提取来自多个源域的通用特征，并经过不同注意力机制的两个自适应模块进行分离，增强全局内容特征与局部风格特征表征；其次，基于AdaIN算法将内容特征与风格特征进行有机融合，进一步提升特征表示，并将融合后的特征输入到特定的分类器和域判别器进行对抗训练；最后，采用平均负样本的半难样本三元组挖掘优化特征嵌入，可以兼顾类内聚集和类间排斥，更好地捕捉真实和伪造类别之间的界限。该方法在四个基准数据集CASIA-FASD、REPLAYATTACK、MSU-MFSD 和 OULU-NPU上进行训练测试，分别达到了6.33%、12.05%、8.38%、10.59%的准确率，优于现有算法，表明该方法能够显著提升人脸活体检测模型在跨数据集测试中的泛化性能。 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
