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

Clicks: 264
ID: 274015
2019
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Abstract
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.
Reference Key
xu2019sensorsaccurate Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chi Xu;Wendi Cai;Yongbo Li;Jun Zhou;Longsheng Wei;Xu, Chi;Cai, Wendi;Li, Yongbo;Zhou, Jun;Wei, Longsheng;
Journal sensors
Year 2019
DOI
10.3390/s20010192
URL
https://www.mdpi.com/1424-8220/20/1/192
Keywords
convolutional neural networks human–computer interaction generative adversarial network hand appearance reconstruction hands detection

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