There are 3 repositories under facial topic.
Facial Landmark Detection based on PyTorch
These are a set of tools using OpenCV, Tensorflow and Keras, with which you can generate your own model of facial landmark detection and demonstrate the effect of newly-generated model easily.
Projects and exercises for the Udacity Computer Vision Nanodegree
Facial Landmark Detection with Caffe CNN. Face alignment avaiable.
Mediante el uso de un script con OpenCV aprenderemos las caras que nos interesen y con otro script arrancaremos la función de reconocimiento. Control de acceso al laboratorio con reconocimiento facial.
A lightweight python implementation of face alignment with MTCNN landmarks using tensorflow-lite runtime
An openface script that runs a REST server. Posted images are compared against a large dataset, and the most likely match is returned. Works with https://hub.docker.com/r/uoacer/openface-mass-compare/
This code is using FER2013 dataset with keras library and tensorflow backend. This code was fork and modified for keras with tensorflow backend from https://github.com/LamUong/FacialExpressionRecognition
Maya rigging scripts to be used with Auri
Smart Glasses for Police Force, a wearable augmented reality glasses with applications in security, medical and industrial field applications such as remote monitoring surgical operations. Our solution is built with state of the art IOT components integrated with Artificial Intelligence. The glasses essentially automate the process of asking for an ID. When the wearer looks at someone, the attached camera apparently takes precise measurements of the person’s face. That measurement is then compared to a database of individuals, each with their own recorded measurements. The tech is reportedly able to determine a match within seconds. For riders, the police want to make sure the person on the train/plane isn’t traveling with someone else’s ID or hopping a train/plane to avoid police. Catching criminals in a real world crowd just with a glance. In the last years, more and more wearable devices are being adapted for law enforcement. Next-generation wearables have the potential to enable police officers to improve situational awareness and decision making during missions. Law enforcement needs real-time information for better situational awareness in the field and at the command center. Officers need access to information, to stream videos and to collaborate in real time.
This code is submitted to ICCV Workshop 2017: Fake vs. true facial emotion recognition competition
Deep Region and Multi-label Learning for Facial Action Unit Detection
Live facial expression to emoji with your webcam.
Facial expression recognition using keras model
Repository cleanup of https://github.com/Blade6570/icface while I play around with it.
🤦♂️ Facial recognition using Ionic 3!
Facial Expression Recognition in Keras using a CNN.
Facial expression detection with a Raspberry Pi
FER - Facial Expression Recognition
Projeto de reconhecimento facial usando React e Firebase.
Play with some pure machine leaning algorithms for solving facial expression recognition.
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!
Facial Recognition using 3D Euclidean Distance and Cosine Similarity Score
68-point facial landmark tracking model for dlib.
On Identifying the characteristic features that will be stored for each subject performing SVD
Machine Learning project to recognize faces from an Image