Emotion Detection with Machine Learning
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, , Yelin Kim : DBN
, Ming Chen : CNN
, ,Hiranmayi Ranganathan : CDBN
,Jufeng Yang : Multitask CNN
, , Emre Dandıl : CNN + Viola-Jones algorithm for face detection
, , Panagiotis Giannopoulos : AlexNet GooLeNet
, , Byoung Chul Ko : review of FER methods, emotion classification in both spatial and temporal, there are to categories: (a) CNN, (b) CNN and LSTM, list of datesets.
, , Charlyn Pushpa Latha : review of (speech and facial) methods, algorithm categories: (a) DBM (b) DNN (c) CNN (c) SAE (f) others
, Dan Duncan : CNN with running average, VGGS network with a face-detector provided by OpenCV (Haar Cascade),
, , Abdulrahman Alreshidi : Haar Cascade + Neighboring Difference Features (NDF)
, , Haik Kalantarian : scalable aggregation of emotive frames from children with autism
Real Time Facial Feature Extraction : Emotional + others, video based, handheld based.
: haar cascade +CNN
: haar cascade + ?
: haar cascade + VGGNet/Resnet
This work showcases two independent methods for recognizing emotions from faces. The first method using representational autoencoder units, a fairly original idea, to classify an image among one of the seven different emotions. The second method uses a 8-layer convolutional neural network which has an original and unique design, and was developed from scratch.
: face recognition, and facial attributes detection (age, gender, emotion and race)
This is a Human Attributes Detection program with facial features extraction. It detects facial coordinates using FaceNet model and uses MXNet facial attribute extraction model for extracting 40 types of facial attributes. This solution also detects Emotion, Age and Gender along with facial attributes.
Real time emotion detection from facial expression using both machine learning and deep learning techniques.
: CNN
: Automatic Micro-Expression Recognition (AMER) This project was about providing an Android application that can help people take charge of their own emotional health by capturing their micro expressions such as happiness, sadness, anger, disgust, surprise, fear, and neutral.
: A Lightweight Deep Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Framework for Python
upgrade approach
upgrade approach
upgrade
: open source, web app
for Drowsiness detection, by aka : Be sure to don't fall asleep when driving thanks to this webapp! You can try it here: , or a
for Expressions reader, by aka : detects 5 high level expressions (happiness, fear, anger, surprise, sadness) from the morph coefficients given by this lib, and display them as smileys. You can try it here: or