A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)

Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

Facial Emotion Recognition using OpenCV and Deepface

Dynamic and static models for real-time facial emotion recognition

face recognition, detection of facial attributes (age, gender, emotion and race) for python.

Facial Emotion Recognition on FER2013 Dataset Using a Convolutional Neural Network

OpenFace 3.0 – open-source toolkit for facial landmark detection, action unit detection, eye-gaze estimation, and emotion recognition.

Human Emotion Analysis using facial expressions in real-time from webcam feed. Based on the dataset from Kaggle's Facial Emotion Recognition Challenge.

Computer Vision module for detecting emotion, age and gender of a person in any given image, video or real time webcam. A custom VGG16 model was developed and trained on open source facial datasets downloaded from Kaggle and IMDB. OpenCV,dlib & keras were used to aid facial detection and video processing. The final system can detect the emotion, age and gender of people in any given image, video or real time webcam

Code for the paper "Facial Emotion Recognition: State of the Art Performance on FER2013"

Classify each facial image into one of the seven facial emotion categories considered using CNN based on https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge

This method achieves SOTA single model accuracy of 73.70 % on FER2013 without using extra training data.

Recognizes the facial emotion and overlays emoji, equivalent to the emotion, on the persons face.

A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (ACL 2023)

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Real-time Facial Emotion Recognition for IT Workplace Productivity using IEEE CLCM Model. Live webcam demo with Flask + TensorFlow. Computer Vision & Machine Learning project.

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.

Detecting Anxiety and Depression using facial emotion recognition and speech emotion recognition. Written in pythonPython

Lightweight Facial Expression(emotion) Recognition model

The main purpose of the project - recognition of emotions based on facial expressions. Cohn-Kanade data set (http://www.pitt.edu/~emotion/ck-spread.htm) is used for explorations and training

facial emotion recognition with CNN and LSTM

This Python-based AI project utilizes OpenCV for facial recognition and a pre-trained deep learning model to analyze facial expressions. By identifying your current mood, the system leverages YouTube's search capabilities to recommend music that aligns with your emotions.

Apache MXNet Gluon implementation for state of the art FER+ paper for Facial Emotion Recognition - https://arxiv.org/abs/1608.01041

This repository provides the codes for MMA-DFER: multimodal (audiovisual) emotion recognition method. This is an official implementation for the paper MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild.

A deep learning and image processing project used to predict the emotions of a person in image.

Code for the paper: "EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition"

This repository demonstrates an end-to-end pipeline for real-time Facial emotion recognition application through full-stack development. The frontend is developed in react.js and the backend is developed in FastAPI. The emotion prediction model is built with Tensorflow Keras, and for real-time face detection with animation on the frontend, Tensorflow.js have been used.

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