Hand gesture provides a means for human to interact through a series of gestures. While hand gesture plays a significant role in human–computer interaction, it also breaks down the communication barrier and simplifies communication process between the general public and the hearing-impaired community. This paper outlines a convolutional neural network (CNN) integrated with spatial pyramid pooling (SPP), dubbed CNN–SPP, for vision-based hand gesture recognition. SPP is discerned mitigating the problem found in conventional pooling by having multi-level pooling stacked together to extend the features being fed into a fully connected layer. Provided with inputs of varying sizes, SPP also yields a fixed-length feature representation. Extensive experiments have been conducted to scrutinize the CNN–SPP performance on two well-known American sign language (ASL) datasets and one NUS hand gesture dataset. Our empirical results disclose that CNN–SPP prevails over other deep learning-driven instances.

手势为人类提供了一种通过一系列手势进行交互的方式。尽管手势在人机交互中起着重要作用，但它也打破了沟通障碍，并简化了公众与听力受损社区之间的沟通过程。本文概述了一种卷积神经网络（CNN）与空间金字塔池（SPP）集成，称为CNN–SPP，用于基于视觉的手势识别。通过将多级池堆叠在一起以将要馈入的要素扩展到完全连接的层中，可以看出SPP可以缓解传统池中的问题。提供不同大小的输入后，SPP还可以生成固定长度的特征表示。已经进行了广泛的实验，以仔细研究两个著名的美国手语（ASL）数据集和一个NUS手势数据集上的CNN–SPP性能。我们的经验结果表明，CNN–SPP胜过其他深度学习驱动的实例。