The Biometric Computing最新文献

Optimization of SVM-Based Hand Gesture Recognition System Using Particle Swarm Optimization and Plant Growth Simulation Algorithm 基于粒子群优化和植物生长模拟算法的svm手势识别系统优化

The Biometric Computing Pub Date : 2019-11-05 DOI: 10.1201/9781351013437-9

K. Sagayam, S. Suresh, D. Hemanth, Lawrence Henessey, C. Ho

引用次数: 2

Deep Neural Networks for Biometric Identification Based on Non-Intrusive ECG Acquisitions 基于非侵入式心电采集的深度神经网络生物特征识别

The Biometric Computing Pub Date : 2019-11-05 DOI: 10.1201/9781351013437-11

João Ribeiro Pinto, Jaime S. Cardoso, A. Lourenço

{"title":"Deep Neural Networks for Biometric Identification Based on Non-Intrusive ECG Acquisitions","authors":"João Ribeiro Pinto, Jaime S. Cardoso, A. Lourenço","doi":"10.1201/9781351013437-11","DOIUrl":"https://doi.org/10.1201/9781351013437-11","url":null,"abstract":"The electrocardiogram has gained traction as a biometric trait due to its outstanding combination of universality, permanence, and measurability, with a hidden nature that makes it harder to steal or counterfeit. The state-of-the-art mostly consists of pipeline algorithms, composed of separate stages of denoising, segmentation, feature extraction, and decision. However, Convolutional Neural Networks (CNNs), possess the tools to integrate all phases of processing, from acquisition to decision, in a single model. This integration replaces separate, step-by-step tuning with an holistic optimisation process, synergically adapting the model to attain the best performance possible. In this chapter, we introduce and explore the capabilities of convolutional neural networks for biometric identification using non-intrusive ECG signal acquisitions, and propose a CNN architecture for the complete integration of traditional pipeline stages in a single accurate and robust model. The method was evaluated on the highly complete and challenging UofTDB collection, and has shown promising results on identification tasks when compared with recent and successful state-of-the-art methods.","PeriodicalId":404463,"journal":{"name":"The Biometric Computing","volume":"457 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132869341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Face Recognition in Low-Resolution Space 低分辨率空间中的人脸识别

The Biometric Computing Pub Date : 2019-11-05 DOI: 10.1201/9781351013437-14

S. S. Rajput, K. V. Arya, Poonam Sharma

引用次数: 0

The Reality of People Re-Identification Task, Where We Are and Where We Are Going 人的再识别任务的现实，我们在哪里，我们要去哪里

The Biometric Computing Pub Date : 2019-11-05 DOI: 10.1201/9781351013437-8

Daniela Moctezuma, Mario Graff, Eric Sadit Tellez, Sabino Miranda-Jiménez

引用次数: 0

Advances in Unconstrained Handprint Biometrics 无约束手印生物识别技术的进展

The Biometric Computing Pub Date : 2019-11-05 DOI: 10.1201/9781351013437-2

Gaurav Jaswal, A. Kaul, R. Nath