A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
Lightweight Facial Expression(emotion) Recognition model
Deep Attentive Center Loss
A Pytorch Implementation of FER( facial expression recognition )
We present our facial expression recognition models for fer-2013 dataset
Recognising expression/emotion of unique faces in a video
🔍 Face Scanning — a Flask application to detect facial expressions: smiling, neutral, and sad. Lightweight, neat, and ready to be used for UX prototypes, emotion testing, or just to check who’s really happy during the pitch. So, it’s not mind reading, but pretty close. 😏
This repository is devoted to the development of the facial emotion recognition (FER) system as a final bachelor project at the TU/e. Realised by Blazej Manczak. Supervisors: Dr. Laura Astola (Accenture) and Dr. Vlado Menkovski (TU/e)
Video Analytics in Python using face-emotion-detection, speech-to-text and text-sentiment analysis pre-trained DEEP LEARNING models
CNN (Convolutional neural network)-based facial expression recognition model (with pretrained weights) implemented with Pytorch
FER - Facial Expression Recognition
TensorFlow is used to build the Facial Expression Recognition (FER) model, which predicts human expression.
Minimal, reproducible webcam demo for POSTER_V2 facial emotion recognition.
Programming Language Translation FER labs
Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild
Testing the Facial Emotion Recognition (FER) algorithm on animations
Tensorflow Implementation of DeepEmotion for Facial Expression Recognition
Laboratory exercises for the Introduction to Theoretical Computer Science course written in Python.
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