摘要
为提高静脉特征提取的有效性,提出了基于稀疏编码的手背静脉识别算法。首先,在图像采集过程中,依据实时的质量评价结果对采集系统参数进行自适应调整,获取高质量静脉图像;其次,针对主观选择的特征有效性主要依赖于经验的缺陷,提出了基于稀疏编码的特征学习机制,从而获得客观优质的静脉特征。实验结果表明,基于所提算法获得的静脉特征具有较好的类间区分性与类内紧凑性,令使用该算法的系统具有较高的识别率。
In order to improve the effectiveness of vein feature extraction, a dorsal hand vein recognition method based on sparse coding was proposed. Firstly, during image acquisition process, acquisition system parameters were adaptively adjusted in real-time according to image quality assessment results, and the vein image with high quality could be acquired. Then concerning that the effectiveness of subjective vein feature mainly depends on experience, a feature learning mechanism based on sparse coding was proposed, thus high-quality objective vein features could be extracted. Experiments show that vein features obtained by the proposed method have good inter-class separableness and intra-class compactness, and the system using this algorithm has a high recognition rate.
出处
《计算机应用》
CSCD
北大核心
2015年第4期1129-1132,1153,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61272214)
辽宁省教育厅资助项目(L2013241)
关键词
静脉识别
质量评价
GABOR变换
稀疏编码
特征优化
vein recognition
quality assessment
Gabor transform
sparse coding
feature optimization
