Python, Computer Vision, Emotion Prediction/Detection, Facial Recognition, Image Annotation (Face, Eyes), DeepFace, CNN, SVM, Real-Time (Webcam) Emotion Detection, Image Manipulation
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Updated
Jan 8, 2023 - Jupyter Notebook
Python, Computer Vision, Emotion Prediction/Detection, Facial Recognition, Image Annotation (Face, Eyes), DeepFace, CNN, SVM, Real-Time (Webcam) Emotion Detection, Image Manipulation
facial emotion recognition
Final project for "Natural Language Processing" course at University of Tartu. December 2017.
Chatbot that assists in streamer-audience interaction made for Twitchcon Hackathon 2017.
An app that allows you to upload images then perform several emotions and will tell you if you are the person in the images
MirrorMoji is a project created for BCHacks 2020 that converts your facial expressions into emojis for fast access.
Emotion recognition using haar-like and CNN model.
An automated cocktailmaker with smart drink recommendations based on the user's emotions and alcohol consumption level.
Facial Emotion Recognition Streamlit App
Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition
Emotion detection using CK+ dataset
Implementation of Realtime Human Face Emotion Recognition, powered by PyTorch
This repository houses a robust speech emotion recognition system, featuring signal processing scripts, machine learning algorithms, and comprehensive documentation. It accurately classifies emotions in spoken language, enabling applications like sentiment analysis and emotion-aware systems.
Realtime Emotion Detection using OpenCV and Tensorflow.
Facial Emotion Recognition is the working of a machine to train itself or recognizing the facial expression from different sources of images of people.
Artificial emotional intelligence or Emotion AI is a branch of AI that allows computers to understand human nonverbal cues such as body language and facial expressions. The aim of this key project is to classify people's emotions based on their facial images.
From physiological signals to emotions through a smart wristband. In this project a program is developed for the creation of the dataset used in human experiments based on eliciting emotions by visualizing images and capturing the signals through the wristband.
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