[1]伊力哈木·亚尔买买提.一种新融合算法的维吾尔族人脸识别[J].智能系统学报,2018,13(03):431-436.[doi:10.11992/tis.201710014]
 Yilihamu·Yaermaimaiti.A new fusion algorithm for uyghur face recognition[J].CAAI Transactions on Intelligent Systems,2018,13(03):431-436.[doi:10.11992/tis.201710014]
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一种新融合算法的维吾尔族人脸识别(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年03期
页码:
431-436
栏目:
出版日期:
2018-05-05

文章信息/Info

Title:
A new fusion algorithm for uyghur face recognition
作者:
伊力哈木·亚尔买买提
新疆大学 电气工程学院, 新疆 乌鲁木齐 830047
Author(s):
Yilihamu·Yaermaimaiti
College of Electncian Engineering, Xinjiang University, Urumqi 830047, China
关键词:
人脸识别维吾尔族光照遮挡离散余弦变换方向边缘幅值模式频域状态深度学习
Keywords:
face recognitionuyghurilluminationocclusiondctpoemfrequency domain statedeep learning
分类号:
TP391.4
DOI:
10.11992/tis.201710014
摘要:
针对维吾尔族人脸在光照以及部分遮挡下的辨识率下降和鲁棒性差的问题,提出了二维离散余弦变换(2DDCT)与方向边缘幅值模式(POEM)相融合的维吾尔族人脸识别算法。首先,把维吾尔族人脸图像分块处理,并使用2DDCT把其分块后的维吾尔族人脸图像转换为频域状态;其次,压缩维吾尔族人脸图像以排除维吾尔族人脸图像中无用信息,即中频部分与非低频部分,并进行二维离散余弦逆变换(IDCT)得到重构的维吾尔族人脸图像;然后,经POEM计算维吾尔族人脸图像的特征量得到其相应的POEM直方图并把直方图级联在一起,作为该中心特征点的POEM纹理直方图,得到维吾尔族人脸特征点的纹理特征信息;最后,采用深度学习算法进行分类识别。本文通过实验提出的算法,在自建的维吾尔族人脸库中能够进一步提高其人脸识别率,在维吾尔族人脸数据库中其运算速度也有很大提高。实验结果表明,该算法尤其是在维吾尔族人脸数据库中拥有较好的识别精度,具有很强的鲁棒性,特别是在光照以及部分遮挡下具有很强的优势。
Abstract:
Considering the inferior robustness of Uyghur face recognition under illumination and partial occlusion, this study proposes a Uyghur face recognition algorithm based on two-dimensional discrete cosine transform (2DDCT) and patterns of oriented edge magnitudes (POEM). The Uygur face images were partitioned into several blocks, and 2DDCT was used to transform the partitioned images into a frequency domain. The images were compacted and irrelevant information was excluded, i.e., the medium-frequency portion and the low-frequency portion, and then a two-dimensional inverse discrete cosine transform (IDCT) was carried out to obtain a reconstructed Uygur face image. The POEM was then used to calculate the characteristic quantity of the Uygur face image to obtain the corresponding POEM histogram. All histograms were cascaded together as the POEM texture histogram of the central characteristic point to acquire the texture feature information of Uygur face feature point. Finally, a deep learning algorithm was used to classify recognition. The algorithm proposed in this paper can improve the face recognition rate and operation speed of a self-built Uyghur face database. Experimental results show that the algorithm has good recognition accuracy, especially for a Uyghur face database, and strong robustness, especially under illumination and partial occlusion.

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备注/Memo

备注/Memo:
收稿日期:2017-10-23。
基金项目:国家自然科学基金项目(61462082).
作者简介:伊力哈木·亚尔买买提,男,1978年生,副教授,主要研究方向为图像处理、模式识别。主持参与国家自然科学基金项目5项,发表核心学术论文数十篇。
通讯作者:伊力哈木·亚尔买买提.E-mail:65891080@qq.com.
更新日期/Last Update: 2018-06-25