Emotion-Detection

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Emotion-detection is an open-source Data & Analytics tool that performs real-time facial emotion detection using deep learning. It classifies emotions into seven categories based on the FER-2013 dataset.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Emotion-detection?

Emotion-detection is an open-source project designed for real-time facial emotion detection utilizing deep learning techniques. It employs deep convolutional neural networks to classify a person's facial emotion into one of seven categories: angry, disgusted, fearful, happy, neutral, sad, and surprised. The model is trained on the FER-2013 dataset, which comprises 35,887 grayscale, 48x48 pixel face images. The implementation uses the Haar cascade method to detect faces in a webcam feed, resizes the facial region to 48x48, and feeds it to the CNN for emotion classification. This tool is compatible with TensorFlow 2.0 and leverages the Keras API, making it suitable for researchers and developers interested in emotion recognition.

Best used for

Ideal for data scientists and professors who need to implement and experiment with real-time facial emotion detection, analyze human emotional responses in video feeds, and develop deep learning applications for affective computing. Especially valuable for research and educational purposes in the field of AI and computer vision.

Common actions

detect emotions

analyze facial expressions

implement deep learning

process real-time video

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Capabilities

Key features

Real-time facial emotion detection
Deep convolutional neural networks
FER-2013 dataset trained
Haar cascade face detection
TensorFlow 2.0 compatible
Keras API utilization

Target Audience

data scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What deep learning framework does Emotion-detection use?

Emotion-detection is built to be compatible with TensorFlow 2.0 and makes extensive use of the Keras API, which is integrated within the TensorFlow library. This allows for efficient development and deployment of the deep convolutional neural networks used for emotion classification.

What emotions can this model detect?

The model is trained to classify facial emotions into seven distinct categories: angry, disgusted, fearful, happy, neutral, sad, and surprised. These categories are based on the FER-2013 dataset, which is a standard benchmark for emotion recognition research.

Does Emotion-detection require a specific dataset for training?

The project is pre-trained on the FER-2013 dataset, but users can also prepare their own datasets. The repository includes a `dataset_prepare.py` file as a reference for converting CSV-formatted data into image files suitable for training, offering flexibility for custom research.

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Research & Education › Academic Research Coding & Development › Open Source & Models

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