A System for Person-Independent Hand Posture Recognition against Complex Backgrounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hand posture recognition in a body-face centered space
CHI '99 Extended Abstracts on Human Factors in Computing Systems
Robust classification of hand postures against complex backgrounds
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Hand Gesture Recognition Using Input-Output Hidden Markov Models
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera
MM '11 Proceedings of the 19th ACM international conference on Multimedia
The Power Is in Your Hands: 3D Analysis of Hand Gestures in Naturalistic Video
CVPRW '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
Learning discriminative representations from RGB-D video data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
We present the LaRED, a Large RGB-D Extensible hand gesture Dataset, recorded with an Intel's newly-developed short range depth camera. This dataset is unique and differs from the existing ones in several aspects. Firstly, the large volume of data recorded: 243, 000 tuples where each tuple is composed of a color image, a depth image, and a mask of the hand region. Secondly, the number of different classes provided: a total of 81 classes (27 gestures in 3 different rotations). Thirdly, the extensibility of dataset: the software used to record and inspect the dataset is also available, giving the possibility for future users to increase the number of data as well as the number of gestures. Finally, in this paper, some experiments are presented to characterize the dataset and establish a baseline as the start point to develop more complex recognition algorithms. The LaRED dataset is publicly available at: http://mclab.citi.sinica.edu.tw/dataset/lared/lared.html.