Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners

细节 指纹(计算) 匹配(统计) 计算机科学 人工智能 模式识别(心理学) 指纹识别 山脊 指纹验证比赛 特征(语言学) Blossom算法 计算机视觉 数学 统计 哲学 古生物学 生物 语言学
作者
Wonjune Lee,Sungchul Cho,Heeseung Choi,Jaihie Kim
出处
期刊:Expert Systems With Applications [Elsevier]
卷期号:87: 183-198 被引量:40
标识
DOI:10.1016/j.eswa.2017.06.019
摘要

Currently, most mobile devices adopt very small fingerprint sensors that only capture small partial fingerprint images. Accordingly, conventional minutiae-based fingerprint matchers are not capable of providing convincing results due to the insufficiency of minutiae. To secure diverse mobile applications such as those requiring privacy protection and mobile payments, a more accurate fingerprint matcher is demanded. This manuscript proposes a new partial fingerprint-matching method incorporating new ridge shape features (RSFs) in addition to the conventional minutia features. These new RSFs represent the small ridge segments where specific edge shapes (concave and convex) are observed, and they are detectable in conventional 500 dpi images. The RSFs are effectively utilized in the proposed matching scheme which consists of minutiae matching and ridge-feature-matching stages. In the minutiae matching stage, corresponding minutia pairs are determined by comparing the local RSFs and minutiae adjacent to each minutia. During the subsequent ridge-feature-matching stage, the RSFs in the overlapped area of two images are further compared to enhance the matching accuracy. A final matching score is obtained by combining the resulting scores from the two matching stages. Various tests for partial matching were conducted on the FVC2002, FVC2004 and BERC (self-constructed) databases, and the proposed method shows significantly lower equal-error rates compared to other matching methods. The results show that the proposed method improves the accuracy of fingerprint recognition, especially for implementation in mobile devices where small fingerprint scanners are adopted.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
Sunshine完成签到,获得积分10
4秒前
Zhang完成签到,获得积分10
4秒前
迷路的松发布了新的文献求助10
7秒前
的速度完成签到,获得积分10
9秒前
10秒前
陈1992完成签到 ,获得积分10
10秒前
10秒前
火火火完成签到 ,获得积分10
12秒前
爱学习的小趴菜完成签到,获得积分10
13秒前
14秒前
Orange应助YY采纳,获得10
15秒前
笑点低的紫蓝完成签到,获得积分10
17秒前
粥粥完成签到,获得积分10
20秒前
华仔应助Sunshine采纳,获得10
20秒前
dpk完成签到,获得积分10
21秒前
22秒前
ding应助苗玉采纳,获得10
22秒前
EL完成签到 ,获得积分10
24秒前
叁壶薏苡发布了新的文献求助80
25秒前
27秒前
27秒前
28秒前
西红柿炒番茄应助小时采纳,获得30
28秒前
31秒前
yegechuanqi发布了新的文献求助10
31秒前
Sunshine发布了新的文献求助10
32秒前
Rita发布了新的文献求助10
35秒前
36秒前
上官若男应助叁壶薏苡采纳,获得10
36秒前
脑洞疼应助火火火采纳,获得10
39秒前
刘阿璐完成签到 ,获得积分10
40秒前
我刚上小学完成签到,获得积分10
46秒前
49秒前
acceleactor完成签到,获得积分10
49秒前
钮幻竹发布了新的文献求助10
49秒前
50秒前
崽崽发布了新的文献求助10
55秒前
tianzml0应助科研人采纳,获得20
55秒前
59秒前
高分求助中
LNG地下式貯槽指針(JGA指-107) 1000
LNG地上式貯槽指針 (JGA指 ; 108) 1000
Generalized Linear Mixed Models 第二版 1000
Preparation and Characterization of Five Amino-Modified Hyper-Crosslinked Polymers and Performance Evaluation for Aged Transformer Oil Reclamation 700
Operative Techniques in Pediatric Orthopaedic Surgery 510
九经直音韵母研究 500
Full waveform acoustic data processing 500
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 免疫学 细胞生物学 电极
热门帖子
关注 科研通微信公众号,转发送积分 2928327
求助须知:如何正确求助?哪些是违规求助? 2578031
关于积分的说明 6956853
捐赠科研通 2228264
什么是DOI,文献DOI怎么找? 1184229
版权声明 589418
科研通“疑难数据库(出版商)”最低求助积分说明 579551