基于LBP特征提取的身份识别优化

T. I. Fajri, Zulaida Rahmi
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引用次数: 0

摘要

单峰系统可用于身份识别系统的信息有限。多式联运系统的建立是为了改进单式联运系统。本研究中使用的多模态系统是面部和手掌在匹配得分水平上的结合。匹配分数使用加权和规则方法完成。使用局部二值模式(LBP)方法从每个样本中提取特征。同时,采用主成分分析(PCA)方法对大数据进行降维。人脸和手掌数据之间的距离使用最近距离,即欧几里得距离法来测量。使用ORL、FERET和理大的基准数据集。通过对各数据库的测试,获得了98% (ORL和PolyU)和95% (FERET和PolyU)的准确率。测试结果表明,采用混合方法(PCA和LBP)的多模态生物识别系统运行良好。关键词:人工智能,识别,LBP,多模态
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IDENTITY RECOGNITION OPTIMIZATION BASED ON LBP FEATURE EXTRACTION
ABSTRACT Unimodal systems have limited information that can be used for identity recognition systems. The multimodal system was created to improve the unimodal system. The multimodal system used in this study is the combination of the face and palms at the matching score level. Matching scores is done using the Weighted Sum Rule method. Extract features from each sample using the Local Binary Pattern (LBP) method. Meanwhile, large data dimensions are reduced by using the Principal Component Analysis (PCA) method. The distance between face and palm data is measured using the closest distance, namely the Euclidean Distance method. Benchmark dataset using ORL, FERET and PolyU. Based on testing on each database, an accuracy rate of 98% (ORL and PolyU) and 95% (FERET and PolyU) is obtained. The test results show that the multimodal system using the Hybrid method (PCA and LBP) biometric system runs well and optimally. Keywords: Artificial intelegency, recognition, LBP, multimodal
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