Biometric Recognition Using Multimodal Physiological Signals

• Published in: IEEE access Vol. 7; pp. 83581 - 83588
• Main Authors: Bianco, Simone, Napoletano, Paolo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biometric identification; Biometrics; convolutional neural network; Electrocardiography; Face recognition; Handwriting recognition; Heart rate; hyperparameters optimization; machine learning; Model accuracy; multimodal physiological signals; Optimization; Physiology; Recognition; Speech recognition
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2923856

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.