Rashmi, M.Guddeti, R.M.R.2026-02-042023Applied Intelligence, 2023, 53, 23, pp. 28711-287290924669Xhttps://doi.org/10.1007/s10489-023-05019-zhttps://idr.nitk.ac.in/handle/123456789/21570Human 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. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Computing powerGait analysisLearning systemsMusculoskeletal systemPattern recognitionAttentionDeep learningFeature extractorGait recognitionHuman identificationLearning modelsLSTMSkeleton dataSmart environmentVisual featureLong short-term memory