摘要
使用非线性混沌方法处理与识别图像的研究工作逐渐增多,已有文献给出了一种将正弦函数作为辅助函数与图像构造动力系统,迭代生成混沌吸引子作为图像特征。为进一步探究图像吸引子作为图像的特性,改进识别效果,使用离散余弦变换(DCT)基函数矩阵代替正弦函数,迭代生成近似混沌吸引子,用于人脸识别。首先,研究分析了DCT基函数矩阵的多样性与振荡特性;然后利用DCT基函数矩阵与图像矩阵构造迭代表达式,通过给出的迭代算法使其产生吸引子,再对吸引子进行快速傅里叶变换,计算相关系数,识别人脸图像。对于Yalefaces图像库,每幅图像都参加训练,识别率可以达到100%,当使用每组前5幅图像训练提取特征,识别率可以超过85%;对于CMU PIE数据库,每幅图像都参加训练,识别率可以超过99%。该吸引子方法可以作为一种图像底层特征提取方法,有待于进一步深入研究。
The research work of image processing and recognition by means of non-linear chaotic method is receiving increasing attention.In the existing literature,there has been a method which constructs dynamic system by taking sinusoidal function as auxiliary function and image,and iteratively generates chaotic attractors as image features.In order to further explore the characteristics of image attractors as image features and improve the recognition effect,this paper uses a discrete cosine transform(DCT)basis function matrix instead of a sine function to generate approximate chaotic attractors iteratively for face recognition.First,this study analyzes the diversity and oscillation of DCT basis function matrix.Then,the DCT basis function matrix and the image matrix are used to construct the iterative expression,and the proposed iterative algorithm is used to generate the attractor.After the attractor is transformed by fast Fourier transform,the correlation coefficient is calculated,and the face image is recognized.For the Yalefaces image database,when each image can be trained,the recognition rate can reach 100%.When the first five images of each group are trained to extract the feature,the recognition rate can exceed 85%.For CMU PIE databases,when each image can be trained,the recognition rate can exceed 99%.And this attractor method can be used as a method of image bottom feature extraction,which still needs further study.
作者
于万波
王香香
王大庆
YU Wan-bo;WANG Xiang-xiang;WANG Da-qing(College of Information,Dalian University,Dalian Liaoning 116622,China)
出处
《图学学报》
CSCD
北大核心
2020年第1期88-92,共5页
Journal of Graphics
关键词
人脸识别
动力系统
混沌吸引子
图像特征
face recognition
discrete cosine transform basis function
chaotic attractor
image features
