[1]孙燕,李旭军,何启泓.跨年龄人脸验证技术研究[J].智能系统学报,2021,16(2):247-253.[doi:10.11992/tis.202011029]
SUN Yan,LI Xujun,HE Qihong.Research on age invariant face verification technology[J].CAAI Transactions on Intelligent Systems,2021,16(2):247-253.[doi:10.11992/tis.202011029]
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SUN Yan,LI Xujun,HE Qihong.Research on age invariant face verification technology[J].CAAI Transactions on Intelligent Systems,2021,16(2):247-253.[doi:10.11992/tis.202011029]
跨年龄人脸验证技术研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第2期
页码:
247-253
栏目:
学术论文—机器学习
出版日期:
2021-03-05
- Title:
- Research on age invariant face verification technology
- Keywords:
- face verification; deep learning; age interference; dual-coded average local binary pattern; histogram of oriented gradient; canonical correlation analysis
- 分类号:
- TP391.41
- 摘要:
- 针对跨年龄人脸验证任务中面部纹理、形状特征变化的问题，提出一种基于双编码平均局部二值模式(dual-coded average local binary pattern,DCALBP)与深度学习算法相结合的多任务人脸验证算法。首先，使用多任务卷积神经网络(multi-task convolutional neural network,MTCNN)对人脸检测图片进行预处理，引入双编码平均局部二值模式(DCALBP)和梯度直方图算法(histogram of oriented gradient,HOG)提取人脸的局部纹理特征和形状特征，运用典型相关性分析(canonical correlation analysis,CCA)算法将两种特征融合，得到人脸年龄特征。然后，孪生网络(siamese network)提取人脸面部特征，并将纹理形状特征从中分离，抑制年龄因素对人脸验证的影响，从而得到具有年龄不变性的人脸特征。最后进行人脸特征匹配，实现跨年龄人脸验证。通过在数据集FG-NET、MORPH Album2以及经过处理的综合数据集上进行实验，准确率分别为89.73%、98.32%和98.27%，充分验证了该方法的有效性。
- Abstract:
- The texture, shape, and other features of the face will change dramatically with age, significantly reducing the accuracy of face recognition. To solve this problem, this study proposes a multitask cross-age face verification algorithm based on the dual coding average local binary pattern (DCALBP) and deep learning algorithm. First, the multitask convolutional neural network is used to detect the face area. Second, the DCALBP and histogram of oriented gradients are used to extract the face texture and shape features, respectively. Then, the canonical correlation analysis is conducted to merge the face texture and shape features to determine the facial age features. Finally, the Siamese network is employed to extract the facial features and separate the facial age features from the facial features, suppress the influence of age factors on face verification, and obtain age-invariant facial features. The algorithm can identify whether it is the same person by feature matching. In this study, the accuracy of the FGNet, MORPH Album2, and processed synthesis data sets is 89.73%, 98.32%, and 98.27%, respectively, and the effectiveness of the proposed method is fully verified.
- 参考文献/References:
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备注/Memo
收稿日期:2020-11-24。
作者简介:孙燕，硕士研究生，主要研究方向为人工智能、计算机视觉、图像处理、人脸识别;李旭军，副教授，博士，主要研究方向为人工智能、计算机视觉、虚拟仪器;何启泓，硕士研究生，主要研究方向为大数据、数据挖掘、图像处理
通讯作者:李旭军.E-mail:xjli@xtu.edu.cn
更新日期/Last Update:
2021-04-25