The current state-of-the-art hand gesture recognition methodologies heavily rely in the use of machine learning. However there are scenarios that machine learning cannot be applied successfully, for example in situations where data is scarce. This is the case when one-to-one matching is required between a query and a dataset of hand gestures where each gesture represents a unique class. In situations where learning algorithms cannot be trained, classic computer vision techniques such as feature extraction can be used to identify similarities between objects. Shape is one of the most important features that can be extracted from images, however the most accurate shape matching algorithms tend to be computationally inefficient for real-time applications. In this work we present a novel shape matching methodology for real-time hand gesture recognition. Extensive experiments were carried out comparing our method with other shape matching methods with respect to accuracy and computational complexity. Our method outperforms the other methods and provides a good combination of accuracy and computational efficiency for real-time applications.

当前最先进的手势识别方法在很大程度上依赖于机器学习的使用。然而，有些场景无法成功应用机器学习，例如在数据稀缺的情况下。在查询和手势数据集之间需要一对一匹配时就是这种情况，其中每个手势代表一个独特的类别。在无法训练学习算法的情况下，可以使用特征提取等经典计算机视觉技术来识别对象之间的相似性。形状是可以从图像中提取的最重要的特征之一，但是最准确的形状匹配算法对于实时应用来说往往计算效率低下。在这项工作中，我们提出了一种用于实时手势识别的新颖形状匹配方法。进行了广泛的实验，将我们的方法与其他形状匹配方法在准确性和计算复杂性方面进行了比较。我们的方法优于其他方法，并为实时应用提供了准确性和计算效率的良好组合。