基于半监督学习的人脸识别反欺骗方法研究

doi:10.11959/j.issn.2096-6652.202138

Abstract

Abstract:

It is a long-term challenge to identify the real and fake faces in the images.When the synthetic fake faces are very realistic, it is difficult for machines and even naked eyes to distinguish the real and fake ones.The supervised anti-spoofing method often requires a large number of labeled samples for a good performance.An anti-spoofing method of face recognition based on semi-supervised learning was proposed to reduce the dependence on massive labeled samples.The method adopted an image inpainting model to learn the data distribution of face images.During the training process, a few labeled samples periodically provided supervised signals to train the classifier to distinguish real faces from fake ones.The proposed method could be used for face anti-spoofing in different scenario, such as faces captured by cameras or generated by generative adversarial net.Accordingly, it was evaluated on the NUAA and RMFD datasets.Experiment results show that the proposed method can keep the quality of restored images, and achieve desirable classification accuracy.With a few labeled samples, the proposed method outperforms Improved-GAN and common semi-supervised methods, and surpasses supervised learning method based on support vector machine and convolutional neural network.

Key words: face anti-spoofing, semi-supervised learning, generative adversarial network, image inpainting

CLC Number:

TP183

Li LI, Weiliang ZENG, Yonghui HUANG, et al. Research on anti-spoofing method of face recognition based on semi-supervised learning[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 370-380.

Figures/Tables 15

方法	100张带标签图像	200张带标签图像	300张带标签图像	所有训练集
SVM	87.36%	89.38%	95.01%	99.80%
CNN	96.55%	98.65%	98.53%	99.96%
本文方法	$98 . 01 %$	$98 . 89 %$	$99 . 21 %$	-

方法	100 张带标签图像	200 张带标签图像	300 张带标签图像	所有训练集
SVM	53.95%	58.20%	62.50%	67.45%
CNN	51.35%	54.80%	59.36%	87.40%
本文方法	$69 . 27 %$	$74 . 55 %$	$83 . 05 %$	-

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网络层类型	卷积核	步长	通道数量/个
卷积层	5×5	2×2	64
卷积层	5×5	2×2	128
卷积层	5×5	2×2	256
卷积层	5×5	2×2	512
卷积层	5×5	2×2	512
卷积层	5×5	2×2	512
全连接层	-	-	1 024

权重值	NUAA	RMFD
1	89.49%	86.95%
0.4	89.14%	87.05%
0.04	88.63%	86.65%
0.004	88.43%	87.80%
0.000 4	89.21%	86.35%
0.000 04	88.82%	86.30%

方法	100张	200张	300张
LS(RBF)	59.53%	59.53%	59.53%
LS(KNN)	71.90%	80.74%	86.68%
LP(RBF)	50.00%	50.00%	50.00%
LP(KNN)	59.65%	59.89%	60.76%
K-means+SVM	92.91%	94.54%	97.82%
Improved-GAN	94.49%	96.07%	96.27%
SHOT-VAE	95.88%	96.12%	98.80%
本文方法	98.01%	98.89%	99.21%

方法	100张	200张	300张
LS(RBF)	49.34%	49.34%	49.34%
LS(KNN)	50.27%	51.80%	53.30%
LP(RBF)	40.47%	40.47%	40.47%
LP(KNN)	50.00%	51.40%	51.60%
K-means+SVM	60.52%	61.08%	62.78%
Improved-GAN	67.14%	69.75%	73.19%
SHOT-VAE	65.05%	67.10%	69.96%
本文方法	62.27%	74.55%	83.05%

	100张	200张	300张
Improved-GAN	61.09%	62.34%	63.96%
SHOT-VAE	58.90%	59.27%	64.15%
本文方法	60.47%	62.96%	64.57%

Research on anti-spoofing method of face recognition based on semi-supervised learning

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References 41

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