IEEE Trans Neural Netw Learn Syst. 2023 Nov;34(11):8978-8988. doi: 10.1109/TNNLS.2022.3154723. Epub 2023 Oct 27.
Gait recognition receives increasing attention since it can be conducted at a long distance in a nonintrusive way and applied to the condition of changing clothes. Most existing methods take the silhouettes of gait sequences as the input and learn a unified representation from multiple silhouettes to match probe and gallery. However, these models are all faced with the lack of interpretability, e.g., it is not clear which silhouette in a gait sequence and which part in the human body are relatively more important for recognition. In this work, we propose a gait quality aware network (GQAN) for gait recognition which explicitly assesses the quality of each silhouette and each part via two blocks: frame quality block (FQBlock) and part quality block (PQBlock). Specifically, FQBlock works in a squeeze-and-excitation style to recalibrate the features for each silhouette, and the scores of all the channels are added as frame quality indicator. PQBlock predicts a score for each part which is used to compute the weighted distance between the probe and gallery. Particularly, we propose a part quality loss (PQLoss) which enables GQAN to be trained in an end-to-end manner with only sequence-level identity annotations. This work is meaningful by moving toward the interpretability of silhouette-based gait recognition, and our method also achieves very competitive performance on CASIA-B and OUMVLP.
步态识别受到越来越多的关注，因为它可以在非侵入的方式下进行长距离识别，并应用于换衣的情况下。大多数现有的方法都将步态序列的轮廓作为输入，并从多个轮廓中学习统一的表示来匹配探针和图库。然而，这些模型都面临着缺乏可解释性的问题，例如，不清楚步态序列中的哪个轮廓和人体的哪个部位对识别更为重要。在这项工作中，我们提出了一种用于步态识别的步态质量感知网络（GQAN），该网络通过两个模块来显式地评估每个轮廓和每个部分的质量：帧质量模块（FQBlock）和部分质量模块（PQBlock）。具体来说，FQBlock 以挤压和激励的方式工作，重新校准每个轮廓的特征，所有通道的得分相加作为帧质量指标。PQBlock 为每个部分预测一个得分，用于计算探针和图库之间的加权距离。特别地，我们提出了一种部分质量损失（PQLoss），使 GQAN 能够仅使用序列级别的身份注释进行端到端训练。通过朝着基于轮廓的步态识别的可解释性方向发展，我们的方法在 CASIA-B 和 OUMVLP 上也取得了非常有竞争力的性能。
