Gait recognition allows identifying people at a distance based on the way they walk (i.e. gait) in a non-invasive approach. Most of the approaches published in the last decades are dominated by the use of silhouettes or other appearance-based modalities to describe the Gait cycle. In an attempt to exclude the appearance data, many works have been published that address the use of the human pose as a modality to describe the walking movement. However, as the pose contains less information when used as a single modality, the performance achieved by the models is generally poorer. To overcome such limitations, we propose a multimodal setup that combines multiple pose representation models. To this end, we evaluate multiple fusion strategies to aggregate the features derived from each pose modality at every model stage. Moreover, we introduce a weighted sum with trainable weights that can adaptively learn the optimal balance among pose modalities. Our experimental results show that (a) our fusion strategies can effectively combine different pose modalities by improving their baseline performance; and, (b) by using only human pose, our approach outperforms most of the silhouette-based state-of-the-art approaches. Concretely, we obtain 92.8% mean Top-1 accuracy in CASIA-B.
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