A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
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Updated
May 26, 2022 - Python
A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
Deep Attentive Center Loss
Lightweight Facial Expression(emotion) Recognition model
Recognising expression/emotion of unique faces in a video
Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild
A Pytorch Implementation of FER( facial expression recognition )
FER - Facial Expression Recognition
Video Analytics in Python using face-emotion-detection, speech-to-text and text-sentiment analysis pre-trained DEEP LEARNING models
Testing the Facial Emotion Recognition (FER) algorithm on animations
University project to learn about FER and machine learning
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)
Laboratory exercises for the Artificial Intelligence course at FER, University of Zagreb (2016/2017).
Laboratory exercises for the Scripting Languages course (Bash, Perl and Python).
Laboratory exercises for the Introduction to Theoretical Computer Science course written in Python.
Bioinformatics project for BCs Thesis @ FER, University of Zagreb (2016/2017).
Programming Language Translation FER labs
Completed exercisms on exercism.io
Face Detection and Emotion Recognition models to capture and interpret facial expressions
TensorFlow is used to build the Facial Expression Recognition (FER) model, which predicts human expression.
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