Exploiting skeleton-based gait events with attention-guided residual deep learning model for human identification

Human identification using unobtrusive visual features is a daunting task in smart environments. Gait is among adequate biometric features when the camera cannot correctly capture the human face due to environmental factors. In recent years, gait-based human identification using skeleton data has been intensively studied using a variety of feature extractors and more sophisticated deep learning models. Although skeleton data is susceptible to changes in covariate variables, resulting in noisy data, most existing algorithms employ a single feature extraction technique for all frames to generate frame-level feature maps. This results in degraded performance and additional features, necessitating increased computing power. This paper proposes a robust feature extractor that extracts a quantitative summary of gait event-specific information, thereby reducing the total number of features throughout the gait cycle. In addition, a novel Attention-guided LSTM-based deep learning model with residual connections is proposed to learn the extracted features for gait recognition. The proposed approach outperforms the state-of-the-art works on five publicly available datasets on various benchmark evaluation protocols and metrics. Further, the CMC test revealed that the proposed model obtained higher than 97% Accuracy in lower-level ranks on these datasets.