| SCUT-DHGA-br |
A Synthetic Database for Dynamic Hand Gesture Authentication. |
2024 |
Access Link |
Cite |
SCUT BIP Lab |
| HaGRIDv2 |
It contains 1,086,158 FullHD RGB images divided into 33 classes of gestures and a new separate “no_gesture” class. |
2024 |
Access Link |
Cite |
SberDevices |
| SCUT-DHGA |
It contains 29,160 dynamic-hand-gesture video sequences and more than 1.86 million frames for both color and depth modalities. |
2023 |
Access Link |
Cite |
SCUT BIP Lab |
| InterHand2.6M |
In total, there are 2,590,347 frames in the 5fps version. |
2020 |
Access Link |
Cite |
Seoul National University |
| MMGatorAuth |
It contains 6 gesture types performed by 106 volunteers. Each gesture type is performed 10 times. In total, there are 10600 RGBD videos. Besides, voiceprint is also provided. |
2020 |
Access Link |
Cite |
INIT Lab (University of Florida) |
| Dynamic Gesture Based User Identification Dataset |
It contains 3 gesture types performed by 60 individuals. Each gesture type is performed 20 times. In total, there are 3600 depth videos. |
2019 |
Access Link |
Cite |
Anhui University of Technology |
| EgoGesture Dataset |
The dataset contains 2,081 RGB-D videos, 24,161 gesture samples and 2,953,224 frames from 50 distinct subjects. |
2018 |
Access Link |
Cite |
Beijing University of Posts and Telecommunications |
| ICVL Hand Posture Dataset |
Each line in this dataset corresponds to one image and contains the (x, y, z) coordinates of the central positions of 16 joints. |
2018 |
Access Link |
Cite |
lmperial College London |
| ASL Alphabet |
The training data set contains 87,000 images which are 200×200 pixels. |
2018 |
Access Link |
Cite |
Akash Nagaraj, a user of Kaggle |
| CMU Panoptic Dataset |
It has rich data sources, covering manually annotated keypoint data, synthetic data, and annotation data obtained from Panoptic Studio. |
2017 |
Access Link |
Cite |
Carnegie Mellon University |
| NVIDIA Dynamic Hand Gesture Dataset |
The dataset consists of approximately 20,000 video clips performed by 20 subjects under diverse illumination and background conditions. |
2016 |
Access Link |
Cite |
NVIDIA |
| ChaLearn LAP IsoGD |
This dataset contains 47,933 RGB-D gesture videos (approximately 9G). |
2016 |
Access Link |
Cite |
CASIA |
| ChaLearn LAP ConGD |
It comprises 22,535 RGB-D gesture videos (approximately 4GB in size), encompassing a total of 47,933 RGB-D gestures. |
2016 |
Access Link |
Cite |
CASIA |
| Handlogin |
It contains 4 gesture types performed by 21 volunteers. Each gesture type is performed 10 times. In total, there are 840 depth videos. |
2015 |
Access Link |
Cite |
VIP Lab (Boston University) |
| Montalbano-Chalearn2014 |
This dataset contains 20 Italian daily-life gestures (e.g., “OK”, “stop”) performed by 27 diverse users. |
2015 |
Access Link |
Cite |
Chalearn |
| UTD Multimodal Human Action Dataset |
The dataset contains 27 actions performed by 8 subjects (4 females and 4 males). |
2015 |
Access Link |
Cite |
University of Texas |
| EgoHands |
It contains 48 videos captured by Google Glass. This dataset covers 4,800 frames and more than 15,000 hands. |
2015 |
Access Link |
Cite |
Indiana University Bloomington |
| The NUS hand posture dataset |
It consists 10 classes of postures, 24 sample images per class. Both greyscale and color images are available (160×120 pixels). |
2013 |
Access Link |
Cite |
NUS |
| MSR Gesture3D |
There are 336 files in total, each corresponding to a depth sequence. |
2012 |
Access Link |
Cite |
Institute of Computing Technology, Chinese Academy of Sciences |