• Ms. Pooja Dewangan
• Mrs. Aditi Mishra
• Mr. L. P. Bhaiya

Touchless hand gesture recognition systems have become vital in automotive user interfaces as they improve safety and luxury. numerous laptop vision algorithms have utilized color and depth cameras for hand gesture recognition, however sturdy classification of gestures from totally different subjects performed below wide varied lighting conditions continues to be difficult. we have a tendency to propose AN algorithmic rule for drivers’ hand gesture recognition from difficult depth and intensity knowledge mistreatment 3D convolution neural networks. Our resolution combines data from multiple spatial scales for the ultimate prediction. It conjointly employs spatiotemporal knowledge augmentation for more practical coaching and to scale back potential overfitting. Our technique achieves an accurate classification rate of seventy seven.5% on the exam challenge dataset.

Hand Gesture Recognition, SLR, CNN, ML.

"Hand Pose Estimation using Convolution Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.467-472, June-2019, Available :http://www.jetir.org/papers/JETIR1906847.pdf

"Hand Pose Estimation using Convolution Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp467-472, June-2019, Available at : http://www.jetir.org/papers/JETIR1906847.pdf