一种用于细粒度人脸识别的眼镜去除方法

毛亮; 薛月菊; 魏颖慧; 朱婷婷

doi:10.11999/JEIT200176

一种用于细粒度人脸识别的眼镜去除方法

doi: 10.11999/JEIT200176

1.
深圳职业技术学院粤港澳大湾区人工智能应用技术研究院深圳 518055
2.
华南农业大学电子工程学院广州 510642
3.
西安电子科技大学数学与统计学院西安 710071

基金项目: 国家科技支撑计划(2015BAD06B03-3)

详细信息

作者简介:
毛亮：男，1983年生，副研究员，研究方向计算机视觉与深度学习

薛月菊：女，1969年生，教授，主要研究方向机器视觉与图像处理计算机视觉与深度学习

通讯作者:
薛月菊　xueyueju@163.com

中图分类号: TN911.73; TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-17
- 修回日期: 2020-07-20
- 网络出版日期: 2020-07-27
- 刊出日期: 2021-05-18

An Eyeglasses Removal Method for Fine-grained Face Recognition

1.
Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic, Shenzhen 518055, China
2.
College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
3.
School of Mathematics and Statistics, Xidian University, Xi’an 710071, China

Funds: The National Science and Technology Support Program (2015BAD06B03-3)

摘要

摘要: 为解决眼镜遮挡会降低人脸识别性能的难点，借鉴深度卷积神经网络在超分辨率方面的成功应用，该文提出一种用于细粒度人脸识别的眼镜自动去除方法ERCNN。用卷积层、池化层、MFM特征选取模块和反卷积层设计ERCNN网络模型，自动学习戴眼镜和未戴眼镜人脸图像对之间的映射关系，实现端到端的眼镜去除。然后，收集大量监控场景下的人脸图像，以及互联网上公开的人脸图像作为训练集；同时构建SLLFW数据集，作为眼镜去除和人脸识别的测试集。最后，通过与传统的眼镜去除方法进行对比试验，该文算法的各项评价指标优于传统方法，能有效的去除真实人脸图像中眼镜；同时在SLLFW人脸数据集上形成的全框眼镜、半框眼镜和无框眼镜人脸数据集上对多种人脸识别算法进行对比试验。试验表明，在FAR为1%的情况下，利用该文方法对F-SLLFW, H-SLLFW和R-SLLFW数据集的人脸图像进行眼镜去除后，SphereFace算法的TAR分别达到90.05%, 91.14%和92.33%，比未去除眼镜的识别率分别提高了3.92%, 3.08%和1.26%；同样，在FAR为0.1%的情况下，比SphereFace算法的TAR分别提高了10.06%, 4.29%和2.13%，说明该文方法有助于提升细粒度人脸识别的识别精度。
- 人脸识别 /
- 深度卷积网络 /
- 特征选择 /
- 眼镜去除
Abstract: In order to solve the problem that eyeglasses reduce often the performance of face recognition, based on the successful application of deep convolution neural network in super-resolution, This paper proposes an automatic eyeglasses removal method ERCNN (Eyeglasses Removal CNN) for fine-grained face recognition. Specifically, the ERCNN network which is designed based on the convolution layer, pool layer, MFM (Max Feature Map)feature selection module and deconvolution layer, are automatically learned the mapping relationship between facial images with eyeglasses and their counterparts without eyeglasses to realize end-to-end eyeglasses removal. Then, massive facial images are captured through surveillance equipment and collected from the Internet as the training set. And, SLLFW data set is established, which is used as the test set of eyeglasses removal and face recognition. The experiment show that the proposed method can better effectively remove the eyeglasses from the real facial image than the traditional eyeglasses removal methods, and the evaluation index of the method is better than other methods. In addition, several face recognition methods are tested separately on the facial images formed by SLLFW data set. Experiments show that when the FAR (False Accept Rate) is 1%, the TAR (True Accept Rate) of the Sphereface method reaches 90.05%, 91.14% and 92.33%, which is 3.92%, 3.08% and 1.26% higher than the Sphereface method is not used to remove the eyeglasses from the F-SLLFW, H-SLLF and R-SLLFW, respectively. Similarly, when the FAR 0.1%, the TAR of Sphereface method is increased by 10.06%, 3.08% and 1.26% respectively. Therefore, the proposed method can better improve the recognition accuracy of fine-grained face recognition.
- Face recognition /
- Deep Convolution Neural Network (DCNN) /
- Feature selection /
- Eyeglasses removal

HTML全文

图 1 戴不同类型眼镜的人脸图像

下载: 全尺寸图片幻灯片

图 2 人脸数据集

下载: 全尺寸图片幻灯片

图 3 ERCNN的网络结构

Conv2a, ···, Conv2d, Conv4为卷积层；ReLU3为激活函数

下载: 全尺寸图片幻灯片

图 4 人脸眼镜去除的结果

下载: 全尺寸图片幻灯片

图 5 真实戴眼镜人脸的眼镜去除的结果

下载: 全尺寸图片幻灯片

图 6 真实戴眼镜人脸的眼镜去除效果不佳的结果

下载: 全尺寸图片幻灯片

表 1 不同方法以及不同类型的眼镜去除性能对比

人脸对齐	眼镜类型	方法	评价指标
人脸对齐	眼镜类型	方法	PSNR (dB)	SSIM	MSE	IFC
128×128	全框	ERCNN	34.7	0.98	22.03	7.0
	全框	PCA	24.2	0.85	247.22	6.1
	半框	ERCNN	35.7	0.99	17.5	7.3
	半框	PCA	25.5	0.87	183.27	6.2
	无框	ERCNN	35.7	0.99	17.5	7.5
	无框	PCA	26.0	0.88	163.34	6.3
	平均值	ERCNN	35.3	0.98	19.01	7.2
	平均值	PCA	25.2	0.86	197.94	6.2
112×96	全框	ERCNN	35.6	0.99	17.91	7.3
	全框	PCA	24.3	0.84	241.59	4.5
	半框	ERCNN	36.3	0.99	15.24	7.6
	半框	PCA	25.0	0.85	205.63	4.5
	无框	ERCNN	36.1	0.99	15.96	7.7
	无框	PCA	24.9	0.85	210.42	4.6
	平均值	ERCNN	36.0	0.99	16.37	7.5
	平均值	PCA	24.7	0.84	219.21	4.5

下载: 导出CSV

表 2 不同人脸识别的识别性能对比(%)

数据集	FAR	TAR
数据集	FAR	①	②	③	④
LFW	1	99.03	98.13	99.07	99.40
LFW	0.1	95.50	91.27	95.90	97.04
SLLFW	1	93.17	78.43	92.53	96.11
SLLFW	0.1	84.33	62.23	85.43	91.38
F-SLLFW	1	80.87	55.77	83.20	86.13
F-SLLFW	0.1	68.27	39.87	70.70	71.73
本文方法对F-SLLFW进行处理后	1	86.47	64.47	88.63	90.05
本文方法对F-SLLFW进行处理后	0.1	77.07	48.10	77.87	81.79
H-SLLFW	1	83.10	60.70	86.27	88.06
H-SLLFW	0.1	72.80	43.53	75.10	78.36
本文方法对H-SLLFW进行处理后	1	87.60	67.07	89.93	91.14
本文方法对H-SLLFW进行处理后	0.1	80.70	45.53	78.83	82.65
R-SLLFW	1	87.07	66.10	89.37	91.70
R-SLLFW	0.1	79.53	49.67	76.73	82.76
本文方法对R-SLLFW进行处理后	1	88.53	67.50	89.13	92.33
本文方法对R-SLLFW进行处理后	0.1	81.07	47.80	77.43	84.89

下载: 导出CSV

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