An AI-based multimodal emotion and stress detection system leveraging computer vision for facial expression recognition and natural language processing for text analysis. Designed for real-time applications using Python, deep learning, and machine learning techniques.

Facial Emotion Recognition using deep residual learning.

Emotion Recognition through CNN is a deep learning model that detects seven facial emotions using a custom ConvNet. It combines features from LBP, ORB, and CNN, achieving up to 98.13% accuracy on JAFFE and 91.01% on FER2013. The model supports real-time emotion detection and is publicly available for research use. Trained with TensorFlow.

[Electronics'21] Facial Emotion Recognition Using Transfer Learning in the Deep CNN"

Facial Expression Recognition using Deep Learning. Keywords: Python, CNN, Image Classification, Keras, TensorFlow, Emotion Detection.

Emotion Recognition is a cutting-edge deep learning project designed to detect and classify human emotions based on facial expressions. Using a Convolutional Neural Network (CNN), the model is trained on the FER2013 dataset and can accurately recognize seven distinct emotions

Facial Emotion Recognition Using Transfer Learning in the Deep CNN

This project is a real-time facial emotion recognition system using OpenCV, Mediapipe, and DeepFace. It captures video from a webcam, detects facial landmarks, and analyzes emotions in real-time using deep learning models.

A deep learning project for facial emotion recognition using CNN architecture trained on the FER2013 dataset. The model classifies facial expressions into 7 emotion categories with comprehensive data augmentation and regularization techniques.

Musition: Real-time emotion-driven music recommendations system using AI and deep learning. Automated facial and hand gesture data collection and emotion recognition training, reducing manual work by 50%. Tech stack: Streamlit-WebRTC, OpenCV/MediaPipe, TensorFlow/Keras, NumPy, Python, JavaScript.

Convolutional Neural Networks (CNNs) are a special category of deep neural networks particularly suited to the analysis of visual imagery – and commonly applied in facial recognition, image classification, and medical imaging. In this session, we’ll demonstrate how to use CNNs to detect a person’s mood and emotions based on their facial expressions. We’ll use TensorFlow in Colab to build, train, and test our model & show you how to refine the process for best results. Prerequisites: —Basic knowledge of Python and machine learning concepts —Google Colab (https://colab.research.google.com/) GitHub Link: ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Social media: —Instagram: http://instagram.com/makesmartthings —Facebook: http://fb.com/makesmartthings —Twitter: http://twitter.com/makesmartthings #DataScience #NeuralNetworks #DeepLearning

😊📸 Real-Time Facial Emotion Recognition using Deep Learning 🤖🧠

Development of a Real-Time Emotion Recognition System Using Facial Expressions and EEG based on machine learning and deep neural network methods

TensorFlow-based deep learning model to classify human emotions from facial expressions using the AffectNet dataset. Includes preprocessing, training, evaluation, and prediction tools. Perfect for exploring emotion recognition and transfer learning.

While tightening and expansion of our facial muscles cause some changes called facial expressions as a reaction to the different kinds of emotional situations of our brain, similarly there are some physiological changes like tone, loudness, rhythm and intonation in our voice, too. These visual and auditory changes have a great importance for human- human interaction human- machine interaction and human- computer interaction as they include critical information about humans’ emotional situations. Automatic emotion recognition systems are defined as systems that can analyze individual’s emotional situation by using this distinctive information. In this study, an automatic emotion recognition system in which auditory information is analyzed and classified in order to recognize human emotions is proposed. In the study spectral features and MFCC coefficients which are commonly used for feature extraction from voice signals are firstly used, and then deep learning-based LSTM algorithm is used for classification.

This project is a basic emotion recognition system that combines OpenAI's GPT API and a deep learning model trained on the FER2013 dataset. It detects facial emotions in real-time from a webcam feed and generates AI responses based on the user's emotion. The project is implemented using TensorFlow, OpenCV, and OpenAI's API

A real-time Face Emotion Recognition (FER) system that uses a deep Convolutional Neural Network (CNN) model to detect mental stress levels during online learning environments. The system captures emotional cues from facial expressions to determine stress, enhancing digital mental health monitoring.

In this study, emotion recognition process is performed by using deep learning methods for seven different facial expressions from the dataset (RidNet) which is created by using images that are publicly accessible from internet. Afterwards, transfer learning over RidNet is done with well-known convolutional neural network architectures such as AlexNet, GoogLeNet and ResNet101. Compound Facial Expressions of Emotion (CE) and Static Facial Expressions in the Wild (SFEW) datasets are determined to be used as test datasets. In the first experimental studies, convolutional neural network architecture with the best classification performance is determined. This convolutional neural network is trained using AffectNet, The Karolinska Directed Emotional Faces and RidNet. Similar classification performances are achieved when the AffectNet, KDEF, and RidNet-trained networks are tested with the dataset (CE) generated in a controlled environment. In the test dataset (SFEW) in an uncontrolled environment, RidNet-trained network gives a significant advantage over the other networks.

Deep learning-based facial expression recognition. Detects emotions from images and real-time video using a CNN model.

Facial emotion recognition API using deep learning | PyTorch, openCV, FastAPI, fer2013, AffectNet

Real-time Human Emotion Detection system using deep learning and facial expression recognition. Built with Python, TensorFlow/Keras, and a user-friendly GUI.

A deep learning project for real-time face detection, emotion recognition, and facial identification using CNNs, implemented during my engineering internship at LEONI WIRING SYSTEMS.

1.1 Project Introduction The Indian education landscape has been undergoing rapid changes for the past 10 years owing to the advancement of web-based learning services, specifically, eLearning platforms. Global E-learning is estimated to witness an 8X over the next 5 years to reach USD 2B in 2021. India is expected to grow with a CAGR of 44% crossing the 10M users mark in 2021. Although the market is growing on a rapid scale, there are major challenges associated with digital learning when compared with brick and mortar classrooms. One of many challenges is how to ensure quality learning for students. Digital platforms might overpower physical classrooms in terms of content quality but when it comes to understanding whether students are able to grasp the content in a live class scenario is yet an open-end challenge. In a physical classroom during a lecturing teacher can see the faces and assess the emotion of the class and tune their lecture accordingly, whether he is going fast or slow. He can identify students who need special attention. Digital classrooms are conducted via video telephony software program (exZoom) where it’s not possible for medium scale class (25-50) to see all students and access the mood. Because of this drawback, students are not focusing on content due to lack of surveillance. While digital platforms have limitations in terms of physical surveillance but it comes with the power of data and machines which can work for you. It provides data in the form of video, audio, and texts which can be analysed using deep learning algorithms. Deep learning backed system not only solves the surveillance issue, but it also removes the human bias from the system, and all information is no longer in the teacher’s brain rather translated in numbers that can be analysed and tracked. 1.2 Problem Statements We will solve the above-mentioned challenge by applying deep learning algorithms to live video data. The solution to this problem is by recognizing facial emotions. 1.2.1 Face Emotion Recognition This is a few shot learning live face emotion detection system. The model should be able to real-time identify the emotions of students in a live class.

Hello Everyone! We are a team of 3 people (Shreya ,Mariam, Maheen ) from the institution of 'Mount Carmel College, Bengaluru'. We have carried out our final semester major project on Facial Emotion Detection Using Deep Learning Techniques which was guided by our mentor Ms. Raahat Ashfaque Shethwala (Digital Analytics Specialist, Lenovo). We were succesfully able to finish the project by preparing 4-5 models by evaluating each model’s accuracy. We implemented the project using neural networks like DCNN, ANN and ML algorithm (SVM ) and also a pre-trained model like 'MobileNet V2' that helped to predict 7 facial expressions. We also have successfully built a live demo scenario where our expressions in real time can be analysed and it gives the output. Please feel free to review & add your comments/ feedbacks/ suggestions. Thank you!

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Facial Emotion Recognition Using Deep Learning and ESP32-CAM

A novel approach for real-time emotion recognition using deep learning to analyze facial expressions and voice through a webcam and microphone.

The Emotion Recognition App is a web-based application that uses deep learning to recognize human emotions from facial expressions. Built with Flask, OpenCV, and TensorFlow,

A real-time and video emotion recognition system that analyzes facial expressions to detect emotions using Deep Learning, FastAPI, and React to create analytics about engagement levels in educational and presentation contexts.

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