https://github.com/hussain-7/emotion_detection-master
Human Emotion Analysis using facial expressions in real-time from webcam feed. Based on the dataset from Kaggle's Facial Emotion Recognition Challenge.
https://github.com/hussain-7/emotion_detection-master
keras-tensorflow matplotlib numpy opencv-python tensorflow
Last synced: about 1 month ago
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Human Emotion Analysis using facial expressions in real-time from webcam feed. Based on the dataset from Kaggle's Facial Emotion Recognition Challenge.
- Host: GitHub
- URL: https://github.com/hussain-7/emotion_detection-master
- Owner: Hussain-7
- Created: 2021-06-06T15:41:49.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-06-10T09:46:35.000Z (about 5 years ago)
- Last Synced: 2025-01-08T06:36:02.293Z (over 1 year ago)
- Topics: keras-tensorflow, matplotlib, numpy, opencv-python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 77.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
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README
## Introduction
This project aims to classify the emotion on a person's face into one of seven categories, using deep convolutional neural networks. The model is trained on the FER-2013 dataset which was published on International Conference on Machine Learning (ICML). This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.
## Algorithm
- First, the haar cascade method is used to detect faces in each frame of the webcam feed.
- The region of image containing the face is resized to 48x48 and is passed as input to the CNN.
- The network outputs a list of softmax scores for the seven classes of emotions.
- The emotion with maximum score is displayed on the screen.
## Folders with class names mapped to numbers for ease
- 0--angry
- 1--disgust
- 2--fear
- 3--happy
- 4--neutral
- 5--sad
- 6--surprise