摘要
针对传统指纹分类算法分类不均衡的缺陷,提出一种基于独立分量分析的二级指纹分类算法。从高阶统计相关性角度出发提取一组特征指纹图像,以该组图像为基,利用该组图像构成的特征空间将指纹图像线性表出,结合系数向量和Henry分类模式将指纹库细分为11个子类,建立二级索引。应用结果表明,该算法可节省运算时间,降低复杂度。
Aiming at the shortage of classification unbalanced in traditional fingerprint classification algorithm,this paper presents a secondary fingerprint classification algorithm based on independent component analysis.It extracts a group of characteristic fingerprint image in terms of high level statistics relevance,uses this group of characteristic as base images,the fingerprint can be projected into the feature space.Combining coefficient vector with Henry classification mode to set up two level index which classifies input fingerprints into eleven kinds of category.Application results show that this algorithm can save operation time and reduce complexity.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第10期16-18,共3页
Computer Engineering
基金
国家自然科学基金资助项目(60375010)
关键词
指纹分类
中心点
三角点
独立分量分析
fingerprint classification
core point
delta point
Independent Component Analysis（ICA）
