# maelfabien/Multimodal-Emotion-Recognition

A real time Multimodal Emotion Recognition web app for text, sound and video inputs

Repository: https://github.com/maelfabien/Multimodal-Emotion-Recognition
Canonical: https://ross.abutalabs.com/products/multimodal-emotion-recognition
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: emotion-analysis, emotion-recognition, emotion-detection, emotions, python, keras, tensorflow, real-time, deep-learning
Last push: 2021-04-29T20:09:29+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2736, "days_push": 1952, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1089, forks 320 (observed 2026-08-28T04:03:32.621760+00:00)

## What it is
A real-time multimodal emotion recognition web app built with Flask that analyzes emotions from text, audio, and video inputs using deep learning models. It combines facial, vocal, and textual analysis through an ensemble model, developed in partnership with the French Employment Agency.

## Use cases
- detect emotions from facial expressions in video
- analyze sentiment and emotion in text
- recognize emotions from voice recordings
- build a multimodal affective computing demo
- run real-time emotion analysis in a web app
- learn deep learning approaches to emotion recognition

## When to choose
- you need a reference implementation combining text, audio, and video emotion models
- you want a ready-to-run Flask web app for real-time emotion analysis
- you are studying multimodal sentiment analysis with Keras/TensorFlow

## When to avoid
- you need a production-grade, actively maintained emotion recognition service
- you require high accuracy on modern benchmarks or recent model architectures
- you need a lightweight library to embed in an existing pipeline rather than a standalone app

## Facets
- artifact type: application
- maturity: maintenance
- function: machine-learning, deep-learning, nlp, audio-processing, computer-vision, data-science
- domain: artificial-intelligence, machine-learning, computer-vision
- platform: python, cross-platform
- tags: emotion-recognition, affective-computing, multimodal, flask, keras, tensorflow, sentiment-analysis, real-time, natural-language-processing, audio, web-server

## Member repositories
- maelfabien/Multimodal-Emotion-Recognition (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:32.621760+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:49:18.604477+00:00, confidence not recorded.
  - readme: https://github.com/maelfabien/Multimodal-Emotion-Recognition (fetched 2026-08-28T04:03:32.621760+00:00, sha 7ec2eade7af2)
- Data as of 2026-08-30T08:39:29.467469+00:00.