A novel biometric identification system based on fingertip electrocardiogram and speech signals
Yükleniyor...
Dosyalar
Tarih
2022-03
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this research work, we propose a one-dimensional Convolutional Neural Network (CNN) based biometric identification system that combines speech and ECG modalities. The aim is to find an effective identification strategy while enhancing both the confidence and the performance of the system. In our first approach, we have developed a voting-based ECG and speech fusion system to improve the overall performance compared to the conventional methods. In the second approach, we have developed a robust rejection algorithm to prevent unauthorized access to the fusion system. We also presented a newly developed ECG spike and inconsistent beats removal algorithm to detect and eliminate the problems caused by portable fingertip ECG devices and patient movements. Furthermore, we have achieved a system that can work with only one authorized user by adding a Universal Background Model to our algorithm. In the first approach, the proposed fusion system achieved a 100% accuracy rate for 90 people by taking the average of 3-fold cross-validation. In the second approach, by using 90 people as genuine classes and 26 people as imposter classes, the proposed system achieved 92% accuracy in identifying genuine classes and 96% accuracy in rejecting imposter classes.
Açıklama
Anahtar Kelimeler
Biometric identification, Biometric identifications, Biometric identification systems, Biometric recognition, Biometrics, Convolutional neural network, Convolutional neural networks, CNN, Electrocardiography, Electrocardiogram signal, Fingertip ECG, Fusion systems, One-dimensional, Performance, Speech, Speech recognition, Speech signals, ECG
Kaynak
Digital Signal Processing: A Review Journal
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
121
Sayı
Künye
Güven, G., Güz, Ü. & Gürkan, H. (2022). A novel biometric identification system based on fingertip electrocardiogram and speech signals. Digital Signal Processing: A Review Journal, 121, 1-13. doi:10.1016/j.dsp.2021.103306
