lock mechanism with face recognition and liveness detection
My experimentation around action recognition in videos. Contains Keras implementation for C3D network based on original paper "Learning Spatiotemporal Features with 3D Convolutional Networks", Tran et al. and it includes video processing pipelines coded using mPyPl package. Model is being benchmarked on popular UCF101 dataset and achieves result…
Repository processes CT scanned images of human Lungs , which are in DICOM image format. Visualises the data in 3D and trains a 3D convolution network on the data after preprocessing.
Gender classification on 3D IXI Brain MRI dataset with Keras and Tensorflow
An experimental project for autonomous vehicle driving perception with steering angle prediction and semantic segmentation using a combination of UNet, attention and transformers.
A python class compatible with TensorFlow to perform data augmentation on 3D objects during CNN training.
This repository contains my personal code for the paper Learning Spatiotemporal Features with 3D Convolutional Networks by Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri.
Experiments for the article "A Comparison of Neural Networks for Sign Language Recognition with LSA64" (JCC 2021)
Imagine you are working as a data scientist at a home electronics company which manufactures state of the art smart televisions. You want to develop a cool feature in the smart-TV that can recognize five different gestures performed by the user which will help users control the TV without using a remote.
Data science Mini projects
A Simple Three Dimensional Convolutional Neural Networks approach
Hand gesture recognition using convolutional neural network and recurrent neural network
The objective of this project is to recognize hand gestures using state-of-the-art neural networks.
Fight Detection From Surveillance Cameras by fine-tuning a PyTorch Pretrained Model
Hand Gesture Recognition using Deep Learning Framework
Real-Time Visual Speech and Emotion Recognition (ViSpEr) an end-to-end neural network for the low-resource visual speech and facial emotion recognition task, using 3D CNNs and LSTMs
Lip reading using TensorFlow, OpenCV, and Keras involves training a deep learning model to recognize spoken words by analyzing lip movements from video frames. The process starts with OpenCV for capturing and preprocessing video frames, focusing on the speaker’s lips. These frames are then fed into a neural network built using Keras and TensorFlow.
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