Discriminative multiscale CNN network for smartphone based robust gait recognition

A smartphone-based gait recognition system is very interesting research in surveillance. Its goal is to recognize a target user from their walking pattern using the inertial signal. However, the performance in realistic scenarios is unsatisfactory due to several covariate factors such as carrying conditions, different surface types, wearing different shoes, wearing different clothes, and also unconstrained placing of mobile phone during walking which affects gait sample data captured by sensors. Recently, many traditional single-scale CNN networks are employed for sensor-based gait recognition. However, these have limited capability to classify only normal gait samples without covariate factors. To address these challenges, in this paper, a novel discriminative Multiscale CNN network (DMSCNN) is designed to introduce both local and global feature extraction procedures for improving classification accuracy. At first, the proposed network discovers the coarse-grained features (local feature) using multiscale CNN analysis to handle different covariate-based variation effects and highlights the significance of local features with respect to class-specific samples by incorporating a class-specific weight update network. Further, fused them to get global features for improving the overall recognition rate. The experiments are performed to evaluate the robustness of the proposed model using four benchmark datasets. The result shows that the proposed model achieves higher accuracy in identification as compared to other state-of-art methods.