期刊
APPLIED SOFT COMPUTING
卷 73, 期 -, 页码 748-766出版社
ELSEVIER
DOI: 10.1016/j.asoc.2018.09.010
关键词
Hand postures; Convolutional neural networks; Deep learning; Hyperparameter selection
Gesture based human-computer interaction is both intuitive and versatile, with diverse applications such as in smart houses, operating theaters and vehicle infotainment systems. This paper presents a novel architecture, combining a convolutional neural network (CNN) and traditional feature extractors, capable of accurate and real-time hand posture recognition. The proposed architecture is evaluated on three distinct benchmark data sets and compared with the state-of-the art convolutional neural networks. Extensive experimentation is conducted using binary, grayscale and depth data, as well as two different validation techniques. The proposed feature fusion-based convolutional neural network (FFCNN) is shown to perform better across combinations of validation techniques and image representation. The recognition rate of FFCNN on binary images is equivalent to grayscale and depth when the aspect ratio of gestures is preserved. A real-time recognition system is presented with a demonstration video. (C) 2018 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据