.png){kind=icon}
Return
Tiny Hand Gesture Recognition without Localization via a Deep Convolutional Network
.png){kind=icon}
PRE
AI
DOI：10.1109/TCE.2017.014971.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Visual hand-gesture recognition is being increasingly desired for human-computer interaction interfaces. In many applications, hands only occupy about 10% of the image, whereas the most of it contains background, human face, and human body. Spatial localization of the hands in such scenarios could be a challenging task and ground truth bounding boxes need to be provided for training, which is usually not accessible. However, the location of the hand is not a requirement when the criteria is just the recognition of a gesture to command a consumer electronics device, such as mobiles phones and TVs. In this paper, a deep convolutional neural network is proposed to directly classify hand gestures in images without any segmentation or detection stage that could discard the irrelevant not-hand areas. The designed hand-gesture recognition network can classify seven sorts of hand gestures in a user-independent manner and on real time, achieving an accuracy of 97.1% in the dataset with simple backgrounds and 85.3% in the dataset with complex backgrounds.
Keywords:
Deep learning
hand gesture recognition
human-machine interface
mobile phone
neural network
no localization
TV
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.9
Papers:
5.3K
Citations:
6.8K
Organization
Cited Papers
Financial flexibility and economic policy uncertainty: Evidence from firm behavior and firm value
PREAI
ShareSave
ShareSaveShareSaveDietary trans and saturated fatty acids effects on semen quality, hormonal levels and expression of genes related to steroid metabolism in mouse adipose tissue.png)
Andrologia
OAAI
AndrologiaIF0
OAAIShareSave
View more