A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
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Updated
Dec 19, 2024 - Python
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
TensorFlow 101: Introduction to Deep Learning
A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
Efficient face emotion recognition in photos and videos
Python library for analysing faces using PyTorch
Automatic 3D Character animation using Pose Estimation and Landmark Generation techniques
Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream
ICPR 2020: Facial Expression Recognition using Residual Masking Network
😆 A voice chatbot that can imitate your expression. OpenCV+Dlib+Live2D+Moments Recorder+Turing Robot+Iflytek IAT+Iflytek TTS
Facial Expression Recognition with a deep neural network as a PyPI package
Face Analysis: Detection, Age Gender Estimation & Recognition
A landmark-driven method on Facial Expression Recognition (FER)
A curated list of facial expression recognition in both 7-emotion classification and affect estimation.
[AAAI'21] Robust Lightweight Facial Expression Recognition Network with Label Distribution Training
Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset
Pytorch implementation of Multi-View Dynamic Facial Action Unit Detection, Image and Vision Computing (2018)
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Frontal Face Images
Human Emotion Analysis using facial expressions in real-time from webcam feed. Based on the dataset from Kaggle's Facial Emotion Recognition Challenge.
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.
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