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Gait recognition refers to the identification of individuals based onfeatures acquired from their body movement during walking. Despite the recentadvances in gait recognition with deep learning, variations in data acquisitionand appearance, namely camera angles, subject pose, occlusions, and clothing,are challenging factors that need to be considered for achieving accurate gaitrecognition systems. In this paper, we propose a network that first learns toextract gait convolutional energy maps (GCEM) from frame-level convolutionalfeatures. It then adopts a bidirectional recurrent neural network to learn fromsplit bins of the GCEM, thus exploiting the relations between learned partialspatiotemporal representations. We then use an attention mechanism toselectively focus on important recurrently learned partial representations asidentity information in different scenarios may lie in different GCEM bins. Ourproposed model has been extensively tested on two large-scale CASIA-B andOU-MVLP gait datasets using four different test protocols and has been comparedto a number of state-of-the-art and baseline solutions. Additionally, acomprehensive experiment has been performed to study the robustness of ourmodel in the presence of six different synthesized occlusions. The experimentalresults show the superiority of our proposed method, outperforming thestate-of-the-art, especially in scenarios where different clothing and carryingconditions are encountered. The results also revealed that our model is morerobust against different occlusions as compared to the state-of-the-artmethods.

View-Invariant Gait Recognition with Attentive Recurrent Learning of Partial Representations