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face-identification
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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 Matching ☑️ Face Liveness Detection ☑️ Face Identification (1:N Face Search) ☑️ Face Attribute Analysis
A collection of face related papers
An End to End Real Time Face Identification and attendance system using CNN
NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof) Android SDK Demo ☑️ Face Recognition ☑️ Face Matching ☑️ Face Liveness Detection ☑️ Face Identification (1:N Face Search) ☑️ Face Pose Estimation
Sample projects for Computer Vision with Raspberry Pi and Movidius Neural Compute Stick
NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof) iOS SDK Demo ☑️ Face Recognition ☑️ Face Matching ☑️ Face Liveness Detection ☑️ Face Identification (1:N Face Search) ☑️ Face Pose Estimation
NIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof), Face Attribute Analysis Windows Server SDK Demo ☑️ Face Recognition ☑️ Face Matching ☑️ Face Liveness Detection ☑️ Face Identification (1:N Face Search) ☑️ Face Attribute Analysis
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, face liveness detection: face matching, face compare, face comparison, face identification, face anti-spoofing, face identity, facial recognition, face representation, face reconstruction, face tracking, and face liveness detection for IDV
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
Face Identification using ONNX Runtime
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.
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