Identification of individuals using functional near‐infrared spectroscopy based on a one‐dimensional convolutional neural network

Abstract

In 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.