A Progressive Focusing-Based Scheme for Low-Quality Fingerprint Pose Estimation

doi:10.3969/j.issn.1671-1122.2025.07.013

Abstract

Abstract:

To address the low accuracy issue of fingerprint pose estimation algorithms in processing low-quality and distorted fingerprints, this paper proposed an ABSF-based progressive focusing scheme. The workflow included: preprocessing raw fingerprint images to obtain texture images; generating enhanced fingerprint images through image enhancement; estimating reference point regions from enhanced images; evaluating positioning accuracy using quality criteria-performing ridge quality enhancement for unqualified cases, or executing final pose estimation for qualified cases. Experimental validation on FVC, NIST SD27 and DF benchmark databases demonstrates superior estimation precision and recognition accuracy compared to existing methods.

Key words: fingerprint recognition, attitude estimation, reference point detection

CLC Number:

TP309

ZHANG Xuefeng, MIAO Kai. A Progressive Focusing-Based Scheme for Low-Quality Fingerprint Pose Estimation[J]. Netinfo Security, 2025, 25(7): 1153-1162.

Figures/Tables 20

References 25

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窗口尺寸	FVC2000				FVC2002
窗口尺寸	DB1	DB2	DB3	DB4	DB1	DB2	DB3	DB4
16$\times $16	—	√	—	—	√	—	√	—
32$\times $32	√	—	√	√	—	√	—	√
窗口尺寸	FVC2004				FVC2006		DF
窗口尺寸	DB1	DB2	DB3	DB4	DB2	DB3	DB4
16$\times $16	—	—	—	—	—	—	—	—
32$\times $32	√	√	√	√	√	√	√	√

方法	姿态定位误差		未检测到	方向误差
方法	误差平均值	标准差	未检测到	误差平均值	标准差
本文方案	18.3%	19.7%	0	-1.4?	26.7?
文献[10]方案	29.9%	44.8%	0	-2.0?	27.3?
文献[23]方案	83.0%	65.5%	0	-7.0?	89.3?
文献[24]方案	144.2%	81.4%	141	N/A	N/A
文献[25]方案	30.4%	78.9%	0	-1.7?	33.8?

指标	本文方案	移除步骤A	移除步骤B	移除步骤C	移除步骤E
平均误差	28.79%	31.54%	31.62%	30.25%	41.05%
标准差	45.78%	48.97%	43.76%	49.14%	55.25%