[1]陆兴华,王凌丰,曾世豪,等.基于神经网络学习的多姿态人脸图像识别算法[J].计算机技术与发展,2019,29(11):57-61.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 012]
　LU Xing-hua,WANG Ling-feng,ZENG Shi-hao,et al.Multi-pose Face Image Recognition Algorithm Based on Neural Network Learning[J].,2019,29(11):57-61.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 012]
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基于神经网络学习的多姿态人脸图像识别算法()
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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11期
页码:
57-61
栏目:
智能、算法、系统工程
出版日期:
2019-11-10

文章信息/Info

Title:
Multi-pose Face Image Recognition Algorithm Based on Neural Network Learning
文章编号:
1673-629X(2019)11-0057-05
作者:
陆兴华; 王凌丰; 曾世豪; 陈家坚
广东工业大学华立学院,广东 广州 511325
Author(s):
LU Xing-hua; WANG Ling-feng; ZENG Shi-hao; CHEN Jia-jian
Huali College Guangdong University of Technology,Guangzhou 511325,Chin
关键词:
人脸识别; 图像; 人工神经网络; 特征提取
Keywords:
face recognition; image; artificial neural network; feature extraction
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 11. 012
摘要:
在移动式拍摄环境下进行人脸识别受到抖动以及环境等因素的影响,导致人脸识别的准确性不好。 因此,文中提出一种基于人工神经网络学习的多姿态人脸图像识别算法。 将空间邻域信息融入到多姿态人脸图像的幅度检测中,提取多姿态人脸图像的动态角点特征,把多姿态人脸图像结构纹理信息类比为一个全局运动 RGB 三维位平面随机场,进行多姿态人脸图像的亮点检测和信息融合。 在不同的尺度下选用合适的特征配准函数来描述多姿态人脸图像的特征点,进行多姿态人脸图像的目标像素视差分析和关键特征检测,结合人工神经网络学习和特征配准方法实现图像稳像处理和自动识别。 仿真结果表明,采用该方法进行多姿态人脸图像识别的特征点配准性能较好,识别精度较高。
Abstract:
Face recognition in mobile shooting is affected by jitter and environment,which leads to poor accuracy of face recognition.Therefore,a multi-pose face image recognition algorithm based on artificial neural network learning is proposed. The spatial neighborhood information is integrated into the amplitude detection of the multi-pose face image,and the dynamic corner feature of the multi-pose face image is extracted. The texture information of the multi-pose face image is compared to a global moving RGB 3D bit plane random field. The bright spot detection and information fusion of multi-pose face image are carried out,and the appropriate feature registration function is selected to describe the feature points of multi-pose face image at different scales. The parallax analysis and key feature detection of multi-pose face image are carried out,and the image stabilization and automatic recognition are realized by combining artificial neural network learning and feature registration method. The simulation shows that the proposed method has better performance and higher recognition accuracy in multi-pose face image recognition.

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更新日期/Last Update: 2019-11-10