论文标题
步态识别通过有效的全局特征表示和局部时间聚集
Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation
论文作者
论文摘要
步态识别是最重要的生物特征识别技术之一，并且已应用于许多领域。最新的步态识别框架代表了从全球外观或人类本地区域提取的描述符代表每个步态框架。但是，基于全球信息的表示通常忽略了步态框架的细节，而基于局部地区的描述符无法捕获邻近地区之间的关系，从而降低了其歧视性。在本文中，我们提出了一种新颖的特征提取和融合框架，以实现步态识别的区分特征表示。为了实现这一目标，我们利用全球视觉信息和局部区域的详细信息，并开发全球和本地功能提取器（GLFE）。具体而言，我们的GLFE模块由我们新设计的多个全球和本地卷积层（GLCONV）组成，以原理方式整合全球和本地特征。此外，我们提出了一种新颖的操作，即局部时间聚集（LTA），以通过减少时间分辨率以获得更高的空间分辨率来进一步保留空间信息。在我们的GLFE和LTA的帮助下，我们的方法显着提高了视觉特征的歧视性，从而改善了步态识别性能。广泛的实验表明，我们提出的方法在两个流行数据集上优于最先进的步态识别方法。
Gait recognition is one of the most important biometric technologies and has been applied in many fields. Recent gait recognition frameworks represent each gait frame by descriptors extracted from either global appearances or local regions of humans. However, the representations based on global information often neglect the details of the gait frame, while local region based descriptors cannot capture the relations among neighboring regions, thus reducing their discriminativeness. In this paper, we propose a novel feature extraction and fusion framework to achieve discriminative feature representations for gait recognition. Towards this goal, we take advantage of both global visual information and local region details and develop a Global and Local Feature Extractor (GLFE). Specifically, our GLFE module is composed of our newly designed multiple global and local convolutional layers (GLConv) to ensemble global and local features in a principle manner. Furthermore, we present a novel operation, namely Local Temporal Aggregation (LTA), to further preserve the spatial information by reducing the temporal resolution to obtain higher spatial resolution. With the help of our GLFE and LTA, our method significantly improves the discriminativeness of our visual features, thus improving the gait recognition performance. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art gait recognition methods on two popular datasets.
