Face emotion recognition technology detects emotions and mood patterns invoked in human faces. This technology is used as a sentiment analysis tool to identify the six universal expressions, namely, happiness, sadness, anger, surprise, fear and disgust. Identifying facial expressions has a wide range of applications in human social interaction d…

Build a full stack application with object-face-emotion recognition

Face Emotion Recognition using FER2013 dataset. Test accuracy: 69.35%

Face Emotion Detection using CNN

Face emotion detection system .

CMP5103 - Artificial Intelligence - Emotion Recognition & Report

Facial Expression Recognition System using YOLOv9 & Flask. Detects 5 emotions (Angry, Happy, Natural, Sad, Surprised) from images/live camera with mAP50 of 0.731. Features a web interface with file uploads, real-time processing, & emoji feedback. Built with Python, OpenCV, Flask, HTML/CSS/JS. Ideal for HCI & emotion analysis.

A real-time facial emotion recognition system using DenseNet121 CNN, detecting emotions like happy, sad, angry, and surprise from webcam or image input. The system maps emotions to emojis, enhancing interaction. Built with TensorFlow, Keras, and OpenCV, this project showcases deep learning in human-computer interaction.

Face-Emotion-Recognition using a model trained over MobileNet with an accuracy of 70%

Face expression detection with face-api

Building and testing several models for real-time facial emotion recognition.

Build a Face Emotion Recognition (FER) Algorithm

Facial Emotion Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face.

Facial emotion recognition (FER) using convolutional neural networks

FaceEmotionRecognition derived by lampadovnikita's archive. An Android application performing recognition of facial emotions on an image.

Recognition of Iranian human facial emotions across four categories: Happy, Sad, Angry, and Neutral.

Recognising facial emotions using a library from pytorch

🧠 Recognize facial emotions using deep learning with Keras and TensorFlow, featuring CNN training, data preprocessing, and visualization tools.

Facial Expression Recognition System using YOLOv9 & Flask. Detects 5 emotions (Angry, Happy, Natural, Sad, Surprised) from images/live camera with mAP50 of 0.731. Features a web interface with file uploads, real-time processing, & emoji feedback. Built with Python, OpenCV, Flask, HTML/CSS/JS. Ideal for HCI & emotion analysis.