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 results similar to those reported by authors

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.

An experimental project for autonomous vehicle driving perception with steering angle prediction and semantic segmentation using a combination of UNet, attention and transformers.

Gender classification on 3D IXI Brain MRI dataset with Keras and Tensorflow

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.

This repo is for the people who want to utilize conv3d for building 3dgan models.

Experiments for the article "A Comparison of Neural Networks for Sign Language Recognition with LSA64" (JCC 2021)

The objective of this project is to recognize hand gestures using state-of-the-art neural networks.

Hand gesture recognition using convolutional neural network and recurrent neural network

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.

A Simple Three Dimensional Convolutional Neural Networks approach

🔍 Read lips in videos with an end-to-end deep learning model, enhancing accessibility and transcribing speech from mouth movements effectively.

Hand Gesture Recognition using Deep Learning Framework

Transform TV control with Gesture Recognition! Enable intuitive interaction with smart TVs using gestures built using Conv3D, CNN & RNN

Context Understanding from Videos analyzes video content by extracting frames and audio, then detecting objects, faces, emotions, and actions. It uses Python with OpenCV, MoviePy, and YOLO. Future plans include embedding models for improved context analysis.

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

Data science Mini projects

Deep learning lip-reading model using Conv3D + BiLSTM + CTC architecture. Transcribes speech from mouth region video clips for accessibility applications.

No description provided.

Develop a conv3D model that can recognise five different gestures performed by the user which will help users control the TV without using a remote

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.

Gesture Recognition

어린이집 CCTV로 학대상황 감지

My team partner and I did this project where we developed a feature in a company’s smart TV that can recognise five different predetermined gestures performed by the user, which will help users control the TV without using a remote.

Hand Gesture Recognition | Convolutional Neural Networks

Develop a cool feature in the smart-TV that can recognise five different gestures performed by the user which will help users control the TV without using a remote.