There are 8 repositories under face-identification topic.
Leading free and open-source face recognition system
Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof), Face Attribute Analysis Linux Server SDK Demo ☑️ Face Recognition ☑️ Face Liveness Detection ☑️ Face Attribute Analysis
A collection of face related papers
NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof) Android SDK Demo ☑️ Face Recognition ☑️ Face Liveness Detection ☑️ Face Pose Estimation
NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof) iOS SDK Demo ☑️ Face Recognition ☑️ Face Liveness Detection ☑️ Face Pose Estimation
NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof), Face Attribute Analysis Windows Server SDK Demo ☑️ Face Recognition ☑️ Face Liveness Detection ☑️ Face Attribute Analysis
Sample projects for Computer Vision with Raspberry Pi and Movidius Neural Compute Stick
An End to End Real Time Face Identification and attendance system using CNN
Code for CVPR 2022 https://arxiv.org/abs/2112.04016 DeepFace-EMD Re-ranking Using Patch-wise Earth Movers Distance Improves Out-Of-Distribution Face Identification
Face identification with cnn+triplet-loss written by Keras.
얼굴 인식에 대한 기술 동향 및 관련 모델 자료
A lightweight face-recognition toolbox and pipeline based on tensorflow-lite
Face Recognition (Identification) for Android Devices. Used Firebase ML Kit Face Detection for detecting faces, then applied arcface MobileNetV2 model for recognition
FRI (Face Recognition and Identification)
Open DRUWA - Open Deep Realtime User Welcoming Assistant
Proiect licenta
This series of workshops on various services is created to help you gain expertise on Azure cognitive and ML Services. You will be able to explore each functionality offered by the service through the GUI & REST APIs and observe the outcomes. We have also shared sample datasets related to each service to replicate what we have built in our workshops. Once you complete these labs, you’ll go from Zero to Hero on the respective service and should be able to Demo, Develop and Deploy your own custom use cases.
The purpose of this Android app is to use Kairos's SDK for Android in order to implement facial recognition. Features of this app include: registering users with an image and name and identifying users when given an image.
Identify unknown persons that are not registered and stores their images with current date and time.
This is the world first repository which describes full solutions for Physical Access Control System containing from hardware design, Face Recognition & Face Liveness Detection (3D Face Passive Anti-spoofing) model to deployment for device. Can be applied to face recognition based smart-lock or similar solution easily.
Intro and sample apps to showcase oneML functionalities and possible use cases: Face Detection, Face Identification, Face Embedding, Vehicle Detection, EKYC, Person Attack Detection.
BWS HTML5 Unified User Interface
Detect, recognize and verify faces using hybrid features: “deep” features from VGG-net + HoG + LBP. Hybrid Features help increase recognition significantly
A simple deepinsight/insightface implementation with FastAPI for face verification.
Face Identification using ONNX Runtime
中科院软件所XLab的机器人UI交互模块
Get ready to activate Secure Access! This app uses face recognition and tracking on live video feed to only allow access to authorized personnel to access a device or premises.
Application for finding the most similar person to you among known celebrities, politicians etc.
🧠 NeuroFace is a Python framework containing tools for detection, human face recognition, analysis of human facial expressions and gestures on video.
One-shot face identification using deep learning
Implement a face recognition using insightface
Face identification algorithms focus on the identifiction of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. It's mostly use for the security.