基于光学衍射场的掌纹识别

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Qing Xiao;Yixuan Wu;Shaohua Tao
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引用次数: 0

摘要

由于其丰富的信息内容和抗伪造性,掌纹识别对于访问控制和法医调查等高安全性应用至关重要。然而,从低质量的掌纹中提取可靠的特征仍然具有挑战性,这在现实世界中很常见,比如犯罪现场的潜在指纹。近年来高分辨率掌纹研究主要集中在图像预处理和特征提取方面;然而,在预处理过程中引入的错误会损害特征的可靠性,从而降低识别的准确性。本文提出了一种基于光学衍射场的方法,从掌纹的光栅状脊纹中提取频域特征。使用结构相似指数、Pearson相关系数和余弦相似度等相似度量来评估特征匹配,并使用随机森林分类器进行决策融合。该方法简化了预处理,降低了计算复杂度,增强了对噪声和变形的鲁棒性。在THUPLMLAB数据集(一个公开可用的高分辨率掌纹数据库)上的实验结果实现了1.00%的相等错误率(EER),与依赖于密集预处理的最先进方法相比,展示了具有竞争力的性能。该方法为生物掌纹识别提供了一种物理可解释、高效、鲁棒的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optical Diffraction Field-Based Palmprint Recognition
Palmprint recognition is critical for high-security applications, such as access control and forensic investigations, due to its rich information content and resistance to forgery. However, extracting reliable features from low-quality palmprints, common in real-world scenarios like latent prints at crime scenes, remains challenging. Recent high-resolution palmprint research has focused on image preprocessing and feature extraction; however, errors introduced during preprocessing can compromise feature reliability, thereby degrading recognition accuracy. In this article, we propose an optical diffraction field-based method that extracts frequency-domain features from the grating-like ridge patterns of palmprints. Feature matching is evaluated using similarity measures including structural similarity index, Pearson correlation coefficient, and cosine similarity, with a random forest classifier for decision fusion. This method simplifies preprocessing, reduces computational complexity, and enhances robustness against noise and deformations. Experimental results on the THUPLMLAB dataset (a publicly available high-resolution palmprint database) achieve an equal error rate (EER) of 1.00%, demonstrating competitive performance against state-of-the-art methods reliant on intensive preprocessing. The proposed method provides a physically interpretable, efficient, and robust solution for biometric palmprint recognition.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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