摘要
人脸识别作为一种关键的生物特征识别技术,目前仍有很多问题需要解决,例如人脸图像特征提取。本文在局部结构图像分解(IDLS)的基础上提出了一种人脸特征提取方法。利用线性回归模型描述局部图像窗口中心宏像素和近邻宏像素之间的结构关系,并得到了局部结构信息。根据图像结构信息,将图像分解成一系列结构图像,并对每个结构图像进行均匀下采样和归一化处理,得到了一个高维的特征向量。最后通过在NUST_RWFR人脸数据库上验证了基于IDLS的人脸图像特征提取方法的有效性,该方法具有较高的识别率,而且计算耗时问题可以通过压缩图像解决。
Face recognition is a key biometric identification technology,and there are still many problems to be solved such as face image feature extraction. This paper presents a robust but simple image feature extraction method,called image decomposition based on local structure( IDLS). It is assumed that in the local window of an image,the macro-pixel( patch) of the central pixel,and those of its neighbors,are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images( also called structure images) according to a local structure feature vector. All the structure images,after being down-sampled for dimensionality reduction,are concatenated into one super-vector. The proposed method is applied to face recognition and examined using our real-world face image database NUST-RWFR。This method has high recognition rate and the computation time consuming problem can be solved by compressing the image.
出处
《激光杂志》
北大核心
2015年第11期71-74,共4页
Laser Journal
基金
河南省科技攻关重点项目(112102310556)
关键词
图像特征提取
局部结构分解
线性回归
人脸识别
image feature extraction
local structure decomposition
linear regression
face recognition
