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facial-expression-recognition.pytorch

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

facial-expression-recognization-using-jaffe

Facial Expression Recognization using JAFFE

facial-expression-recognizer

Recognize facial expression using CNNs

facial-recognition

recognize facial expressions of human beings with accurate rate 99% on a data set of about 10000 expressions of 30 people by using Python

facialemotionrecognition

Using Extended Cohn-Kanade AU-Coded Facial Expression Database to classify basic human facial emotion expressions using ann

facialexpressionnao

Facial Expression Recognition Using NAO

facialexpressionrec

Cross-Database Facial Expression Recognition Based on Fine-Tuned Deep Convolutional Network

facialexpressionrecognition

Deep learning for the facial expression recognition kaggle challenge

facialexpressionrecognition-1

Contrast multiple facial expression recognition experiments and found that using SVM instead of softmax layer can achieve better classification results(65.47% accuracy on fer2013 dataset).

facialexpressionrecognition-2

Fer2013 Facial Expression Recognition Keras

facialrecognition

Facial Expression Recognition with TensorFlow

facialrecognition-1

Recognize Human Expressions using CNN and SVM

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit

fashion-mnist-cnn-keras

Zalando's article images Recognition using Convolutional Neural Networks in Python with Keras.

fast-rcnn

Fast R-CNN

fatigue-detection

Detect the drivers fatigue real-time using python and OpenCV

fatigue-driven-detection-based-on-cnn

本科毕设内容：基于卷积神经网络的疲劳驾驶检测。代码及自制数据集。

fatigue-testing

contribute by Lu Hui Ren Teng

fatiguedetection

This is the fatigue driving test test code.

fatiguedetection-1

fatiguedriving

疲劳驾驶检测

fatiguemonitoring

疲劳驾驶检测

fer

Facial Expression Recognition based on Convolutional Neural Networks and Transfer learning

fer-using-dnn

facial expression recognition using CNN and SIFT

focusing-attention-of-fully-convolutional-neural-networks-on-region-of-interest-roi-input-map-

This project contains code for a fully convolutional neural network (FCN) for semantic segmentation with a region of interest (ROI) map as an additional input (Figure 1). The net receives image and ROI as a binary map with pixels corresponding to ROI marked 1, and produce pixel-wise annotation of the ROI region of the image. The method is based on using region selective features This code was tested on for semantic segmentation task of materials in transparent vessels where the vessel area of the image was set as the ROI.

gabor-filter-face-extraction

Extract face feature based on gabor filtering

generative-adversarial-network-for-faces

A generative adversarial network for generating images of faces using the CK+ dataset

grammatical-facial-expression-recognition

Recognizing grammatical facial expressions using customized deep neural network architecture

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

heartrate_eyeblink_detect_fatigue

使用眨眼+低功率蓝牙模块检测疲劳，检测到疲劳后发出语音干预，可播放声音。Python+Qt