IEEE Trans Cybern. 2018 May;48(5):1526-1539. doi: 10.1109/TCYB.2017.2705799. Epub 2017 Jun 5.
Gait is a commonly used biometric for human recognition. Its main advantage relies on its ability to identify people at distances at which other biometrics fail. In this paper, we develop a new approach for gait recognition that combines the distance transform with curvatures of local contours. We call our gait feature template the normal distance map. Our method encodes both body shapes and boundary curvatures into a novel feature descriptor that is more robust than existing gait representations. We evaluate our approach on the widely used and challenging USF and CASIA-B datasets. Furthermore, we evaluate it on the OU-ISIR gait dataset, the largest one available in the literature, to obtain statistically reliable results. We verify our approach is significantly superior to the current state-of-the-art under most conditions.
步态是一种常用的人体识别生物特征。它的主要优势在于能够在其他生物特征无法识别的距离识别人员。在本文中，我们开发了一种新的步态识别方法，该方法将距离变换与局部轮廓的曲率相结合。我们将我们的步态特征模板称为正常距离图。我们的方法将身体形状和边界曲率编码到一种新的特征描述符中，该描述符比现有的步态表示更稳健。我们在广泛使用的具有挑战性的 USF 和 CASIA-B 数据集上评估我们的方法。此外，我们在 OU-ISIR 步态数据集上对其进行评估，该数据集是文献中最大的数据集，以获得具有统计学意义的可靠结果。我们验证了我们的方法在大多数情况下明显优于当前的最先进技术。
