Accurate Hand Detection from Single-Color Images by Reconstructing Hand Appearances

Sensors

Jun Zhou¹  Yongbo Li¹  Chi Xu¹  Longsheng Wei¹  Wendi Cai¹ 

[1] School of Automation, China University of Geosciences, Wuhan 430074, China;

关键词: hands detection;    hand appearance reconstruction;    convolutional neural networks;    generative adversarial network;    human–computer interaction;   

DOI  :  10.3390/s20010192

来源: DOAJ

【 摘 要 】

Hand detection is a crucial pre-processing procedure for many human hand related computer vision tasks, such as hand pose estimation, hand gesture recognition, human activity analysis, and so on. However, reliably detecting multiple hands from cluttering scenes remains to be a challenging task because of complex appearance diversities of dexterous human hands (e.g., different hand shapes, skin colors, illuminations, orientations, and scales, etc.) in color images. To tackle this problem, an accurate hand detection method is proposed to reliably detect multiple hands from a single color image using a hybrid detection/reconstruction convolutional neural networks (CNN) framework, in which regions of hands are detected and appearances of hands are reconstructed in parallel by sharing features extracted from a region proposal layer, and the proposed model is trained in an end-to-end manner. Furthermore, it is observed that the generative adversarial network (GAN) could further boost the detection performance by generating more realistic hand appearances. The experimental results show that the proposed approach outperforms the state-of-the-art on public challenging hand detection benchmarks.

【 授权许可】

Unknown