1. [Publications](/index.php/publications) 2. Hand Gesture Recognition with 3D Convolutional Neural Networks # Hand Gesture Recognition with 3D Convolutional Neural Networks ![](/sites/default/files/styles/wide/public/pubs/2015-06_Hand-Gesture-Recognition/cvprw-3dcnn-hand.png?itok=-tYtlJZC) Touchless hand gesture recognition systems are becoming important in automotive user interfaces as they improve safety and comfort. Various computer vision algorithms have employed color and depth cameras for hand gesture recognition, but robust classification of gestures from different subjects performed under widely varying lighting conditions is still challenging. We propose an algorithm for drivers’ hand gesture recognition from challenging depth and intensity data using 3D convolutional neural networks. Our solution combines information from multiple spatial scales for the final prediction. It also employs spatio-temporal data augmentation for more effective training and to reduce potential overfitting. Our method achieves a correct classification rate of 77.5% on the VIVA challenge dataset. ## Authors [Pavlo Molchanov](/index.php/person/pavlo-molchanov) [Shalini Gupta](/index.php/person/shalini-de-mello) Kihwan Kim (NVIDIA) [Jan Kautz](/index.php/person/jan-kautz) ## Publication Date Monday, June 1, 2015 ## Published in [IEEE Computer Vision and Pattern Recognition Workshop (CVPRW) 2015](http://www.pamitc.org/cvpr15/) ## Research Area [Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence) [Computer Vision](/index.php/research-area/computer-vision) ## Uploaded Files [CVPRW2015-3DCNN.pdf](https://research.nvidia.com/sites/default/files/pubs/2015-06_Hand-Gesture-Recognition/CVPRW2015-3DCNN.pdf "Open file in new window")583.07 KB ## Award Winner (1st place) Hand Gesture Recognition Challenge ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .