Affiliation:
1. South China Academy of Advanced Optoelectronics South China Normal University Guangzhou China
Abstract
AbstractIn recent years, the application of functional near‐infrared spectroscopy (fNIRS) and deep learning techniques has emerged as a promising method for personal identification. In this study, we innovatively utilized a deep learning framework and fNIRS data for personal identification. The framework is a one‐dimensional convolutional neural network (Conv1D) trained on resting‐state fNIRS signals collected from the frontal cortex of adults. In data preprocessing, we employed a sliding window‐based data augmentation technique and high‐pass filter, which could result in the highest identification accuracy through multiple experiments. Based on a data set consisting of 56 adult participants, the identification accuracy of 90.36% is achieved for training data with a window size of approximately 4.62 s; with the increase in training data window size, the identification accuracy can reach (97.65 ± 2.35)%. Our results suggest that deep learning is valuable for fNIRS‐based personal identification, with potential applications in security, biometrics, and healthcare.
Funder
National Natural Science Foundation of China
Subject
General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry