Name
Title | ||
|---|---|---|
Physiology-based augmented deep neural network frameworks for ECG biometrics with short ECG pulses considering varying heart rates |
Abstract | ||
|---|---|---|
•ECG is a promising biometric method with high performance, liveness test capability, and wearable sensor availability.•Varying heartbeat rates is a major issue in ECG biometrics, leading to the performance degradation.•We propose a physiology-based augmented DNN framework for ECG biometrics with short pulses to encapsulate any DNN methods.•Our framework has enhanced DNN ECG biometrics; up to 11.7% improvement in accuracy over multiple sessions. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patrec.2022.02.014 | Pattern Recognition Letters |
Keywords | DocType | Volume |
Biometrics,Electrocardiogram,QT interval correction,Deep learning,Short ECG pulses | Journal | 156 |
ISSN | Citations | PageRank |
0167-8655 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanvit Kim | 1 | 0 | 0.34 |
| Thanh Quoc Phan | 2 | 0 | 0.34 |
| Wonjae Hong | 3 | 0 | 0.34 |
| Se Young Chun | 4 | 72 | 18.18 |