Citation
Bao, Peijun and Maqueda Nieto, Ana Isabel and Blanco Adán, Carlos Roberto del and García Santos, Narciso
(2017).
Tiny hand gesture recognition without localization via a deep convolutional network.
"IEEE Transactions on Consumer Electronics", v. 63
(n. 3);
pp. 251-257.
ISSN 0098-3063.
https://doi.org/10.1109/TCE.2017.014971.
Abstract
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.