[1]张媛媛,霍静,杨婉琪,等.深度信念网络的二代身份证异构人脸核实算法[J].智能系统学报,2015,10(02):193-200.[doi:10.3969/j.issn.1673-4785.201405060]
 ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10(02):193-200.[doi:10.3969/j.issn.1673-4785.201405060]
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深度信念网络的二代身份证异构人脸核实算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年02期
页码:
193-200
栏目:
出版日期:
2015-04-25

文章信息/Info

Title:
A deep belief network-based heterogeneous face verification method for the second-generation identity card
作者:
张媛媛 霍静 杨婉琪 高阳 史颖欢
南京大学 计算机软件新技术国家重点实验室, 江苏 南京 210023
Author(s):
ZHANG Yuanyuan HUO Jing YANG Wanqi GAO Yang SHI Yinghuan
State Key Laboratory for Software Technology, Nanjing University, Nanjing 210023, China
关键词:
人脸核实多模态深度学习深度信念网络
Keywords:
face recognitionmultimodesdeep learningdeep belief network
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201405060
文献标志码:
A
摘要:
二代身份证人脸核实问题是指判断二代身份证人像和身份证使用者当前头像是否为同一人。具体来说,即将二代身份证模糊人像和实际在光照、背景等因素不可控环境下拍摄的若干张二代证使用者的视频人像作匹配,判断是否为同一个人。由于低分辨率模糊图像和清晰视频图像属于2种不同的图像模态,因此该问题属于异构人脸识别问题。考虑到跨模态人脸图像的差异,传统的特征抽取方法很难抽取判别性特征来描述不同模态图像,使得传统方法难以达到精准辨别。针对这个问题,提出了一种新的基于深度学习的解决方法,其基本思想是通过深度信念网络(DBN)的非监督贪心逐层训练来提取人脸图像的高层特征,结合传统的图像预处理和相似性度量技术,达到人脸核实的目的。通过在256人的真实二代证数据集上和传统特征降维方法PCA、LDA进行比较,证实了所提出方法在准确率上相比PCA有约12%的提升,相比LDA有约8%的提升。实验同时表明,针对数据量增大的情况,基于深度学习的解决方法要优于传统的人脸识别方法。
Abstract:
The objective of the face verification method for the second-generation identity card is to determine whether the original head-photo stored in the corresponding identity card image and the currently captured head photo of the card-holder by using a video camera image actually belongs to the same person or not. To obtain a good verification result for the heterogeneous face verification method is a very challenging task because the two different types of images belong to two different modalities (e.g., different image resolutions, different illumination conditions). Considering the difference of trans-modal face images, it is hard to use traditional feature extraction methods to extract discriminative feature for description of images with different modes. Traditional feature extraction methods cannot distinguish images exactly. In this paper, a deep learning-based face verification method is proposed. The proposed deep learning-based face verification method integrates the deep belief network (DBN), which employs unsupervised greedy layer-by-layer training for high-level feature extraction of face photo and combines the popularly used image preprocessing and similarity measurement technologies to realize the purpose of face verification. The results were evaluated on a real dataset with two different modalities of 256 different people. This method outperforms the traditional principal component analysis (PCA) and linear discriminant analysis (LDA) methods with 12% and 8% improvements in terms of the verification accuracy, respectively. The results validated the advantage of the proposed method, especially when the amount of entries increases.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-5-28;改回日期:。
基金项目:国家自然科学基金资助项目(61035003,61175042).
作者简介:张媛媛,女,1991年生,硕士研究生,主要研究方向为机器视觉、机器学习等;杨婉琪,女,1988年生,博士研究生,主要研究方向为机器学习、机器视觉等;高阳,男,1972年生,教授,博士生导师,主要研究方向为强化学习、智能agent、智能应用等。
通讯作者:张媛媛.E-mail:zhangyuanyuan2013nju@gmail.com.
更新日期/Last Update: 2015-06-15