In this paper, we provide a new spatial data generalization method which we applied in hand gesture recognition tasks. Data gathering can be a tedious task when it comes to gesture recognition, especially dynamic gestures. Nowadays, the standard solutions when lacking data still consist of either the expensive gathering of new data or the impractical employment of hand-crafted data augmentation algorithms. While these solutions may show improvement, they come with disadvantages. We believe that a better extrapolation of the limited data’s common pattern, through an improved generalization, should first be considered. We, therefore, propose a dynamic generalization method that allows to capture and normalize in real-time the spatial evolution of the input. The latter procedure can be fully converted into a neural network processing layer which we call Evolution Normalization Layer. Experimental results on the SHREC2017 dataset showed that the addition of the proposed layer improved the prediction accuracy of a standard sequence-processing model while requiring 6 times fewer weights on average for a similar score. Furthermore, when trained on only 10% of the original training data, the standard model was able to reach a maximum accuracy of only 36.5% alone and 56.8% when applying a state-of-the-art processing method to the data, whereas the addition of our layer alone permitted to achieve a prediction accuracy of 81.5%.

在本文中，我们提供了一种新的空间数据泛化方法，我们将其应用于手势识别任务。当涉及到手势识别时，数据收集可能是一项乏味的任务，尤其是动态手势。如今，缺乏数据的标准解决方案仍然包括昂贵的新数据收集或手工制作的数据增强算法的不切实际的使用。虽然这些解决方案可能会有所改进，但它们也有缺点。我们认为，首先应该考虑通过改进的概括来更好地推断有限数据的共同模式。因此，我们提出了一种动态泛化方法，可以实时捕获和规范输入的空间演变。进化归一化层。在 SHREC2017 数据集上的实验结果表明，所提出的层的添加提高了标准序列处理模型的预测精度，同时对于相似的分数，平均需要的权重减少了 6 倍。此外，当仅对原始训练数据的 10% 进行训练时，标准模型仅能达到 36.5% 的最大准确率，在对数据应用最先进的处理方法时达到 56.8%，而仅添加我们的层就可以实现 81.5% 的预测准确率。