Personal Identification Using Gait Spectrograms and Deep Convolutional Neural Networks

Abstract

Human gait can serve as a useful behavioral trait for biometrics. Compared to fingerprint, face, and iris, the most commonly used physiological identifiers, gait can be unobtrusively monitored from a distance without requiring explicit involvement and physical restraint from people. Advances in wearable technology facilitate direct and faithful measurement of gait motions with easy-to-use and low-cost inertial sensors. This study aimed to propose an approach to using kinematic gait data collected with wearable inertial sensors for reliable personal identification. Sixty-nine individuals ranged in age from 24 to 62 years old participated in this study. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the feet, shanks, thighs, and posterior pelvis while walking. The gait spectrograms were acquired by applying time-frequency analyses to the lower body movement signals measured in one stride. Among each participant's 15 strides, 12 strides were used in the 4-fold cross validation of the deep convolutional neural network-based classifiers, and the remaining three strides were used to evaluate the classifiers. An accuracy of 99.69% was achieved by using the foot, shank, thigh, and pelvic spectrograms, and the accuracy was 96.89% using only the foot spectrograms. This study has the potential to be applied in behavior-based biometric technologies by confirming the feasibility of the proposed kinematic and spectrographic approaches in identifying personal behavioral characteristics.