[1]贺辉,陈思佳,黄静.一种改善光照对深度人脸识别影响的方法[J].计算机技术与发展,2019,29(04):38-41.[doi:10. 3969 / j. issn. 1673-629X. 2019. 04. 008]
　HE Hui,CHEN Si-jia,HUANG Jing.An Improved Illumination Approach in Deep Face Recognition[J].,2019,29(04):38-41.[doi:10. 3969 / j. issn. 1673-629X. 2019. 04. 008]
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一种改善光照对深度人脸识别影响的方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年04期
页码:
38-41
栏目:
智能、算法、系统工程
出版日期:
2019-04-10

文章信息/Info

Title:
An Improved Illumination Approach in Deep Face Recognition
文章编号:
1673-629X(2019)04-0038-04
作者:
贺辉; 陈思佳; 黄静
北京师范大学珠海分校 信息技术学院,广东 珠海 519087
Author(s):
HE Hui; CHEN Si-jia; HUANG Jing
School of Information Technology,Beijing Normal University,Zhuhai,Zhuhai 519087,China
关键词:
人脸识别; 深度学习; 光照; 视网膜大脑皮层增强
Keywords:
face recognition; deep learning; illumination; Retinex reinforcement
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 04. 008
摘要:
在人脸识别领域,消除光照变化的不利影响一直以来都是一个难以解决的问题。而与过去的机器学习模型不同,深度学习模型的结构具有和人类视觉神经结构相似的特性。这虽然使模型表现出了非常好的识别效果,但也使模型变得难以解释,以至于以往的人脸光照预处理方法不再可靠。考虑到卷积神经网络具有生物视觉神经的特点,文中在带彩色恢复的多尺度视网膜增强(MSRCR)方法的基础上,结合对比度增强处理,提出了一种类视网膜大脑皮层增强法,以改善基于深度学习的人脸识别模型中光照不均造成的错误识别问题。同时,与基于子空间统计的方法、基于光照不变表示的方法、基于直方图均衡化方法进行了多组对比实验,结果显示该方法比其他方法更有效,可使深度学习模型的识别率显著提高。
Abstract:
It has always been a difficult problem to eliminate the adverse effects of varying illumination in face recognition. Different fromexisted machine learning models,the structure of deep learning model is similar to that of human visual nerve. This makes the modelshow better recognition effect,but also makes it difficult to explain,so that the previous face illumination pretreatment method is no longer reliable. Therefore,considering the convolutional neural network owning characteristics of biological visual nerve,on the basis of multiscale Retinex with color restoration (MSRCR),combining contrast enhancement processing,we propose a Retinex enhancement methodto improve the error identification problem caused by uneven illumination in face recognition model based on deep learning. And compared with the methods based on subspace statistics,illumination invariant representation and histogram equalization,the results show thatthis method is more effective than other methods,and can significantly improve the recognition rate of the deep learning model.

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更新日期/Last Update: 2019-04-10