A classifier based on multiple feature extraction blocks for gait authentication using smartphone sensors

A convolutional neural network model for gait identification in the sensor domain with multiple feature extraction blocks (MFEBP) is proposed, using Residual Block and Squeeze-and -Excitation Blocks. NJUPT Datasets I, II and III have been established, containing acceleration and gyroscope signals from 113 subjects using sensors built into smartphones, in different parts of body. Dataset I is employed to pinpoint the best position of smartphone for recognition. The other two are taken to verify the performance of the designed model. The experimental results of Dataset I show that waist part is the most stable position for identification. Several experiments are also conducted to evaluate the MFEBP on OU-ISIR, ZJU, NJUPT Dataset II, III and whuGait Dataset 1, 2. The classifier with the best accuracy achieves 98.56%, 93.48%, 94.17%, 98.47%, 95.38% and 97.93% separately at a sliding length of 50 sample points. Moreover, this paper also investigates data enhancement by varying the sliding length. In the OU-ISIR dataset, the accuracy increases from 83.56% to 99.91% as the sliding length reduces from 50 to 10 sample points. In conclusion, three gait datasets are constructed and a gait model with higher recognition accuracy was proposed.