Name
Abstract | ||
|---|---|---|
In this paper, we address the problem of biometric recognition using the multimodal physiological signals. To this end, four different signals are considered: heart rate (HR), breathing rate (BR), palm electrodermal activity (P-EDA), and perinasal perspitation (PER-EDA). The proposed method consists of a convolutional neural network that exploits mono-dimensional convolutions (1D-CNN) and takes as input a window of the raw signals stacked along the channel dimension. The architecture and training hyperparameters of the proposed network are automatically optimized with the sequential model-based optimization. The experiments run on a publicly available dataset of multimodal signals acquired from 37 subjects in a controlled experiment on a driving simulator show that our method is able to reach a top-1 accuracy equal to 88.74% and a top-5 accuracy of 99.51% when a single model is used. The performance further increases to 90.54% and 99.69% for top-1 and top-5 accuracies, respectively, if an ensemble of models is used. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2923856 | IEEE ACCESS |
Keywords | Field | DocType |
Biometric identification,multimodal physiological signals,machine learning,convolutional neural network,hyperparameters optimization | Handwriting,Computer science,Speech recognition,Biometrics,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 1 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Bianco | 1 | 226 | 24.48 |
| Paolo Napoletano | 2 | 339 | 37.19 |