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davidsandberg / facenet

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Instructions How to move further with facenet

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atiato
atiato commented Aug 7, 2018

Dears,

if somebody has instructions , my target is to recognize a face using tensor flow but I didn't find a full instructions which scripts to use to do the following :
1- crop faces
2- Train images .
3- face recognition.

if examples or sample commands that would be appreciated!

I have Ubuntu Linux with python and tensor flow environment ready.

appreciate your steps to go forward

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serengil / deepface

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A Lightweight Deep Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Framework for Python

python machine-learning deep-learning tensorflow keras face-recognition openface facenet face-analysis facial-expression-recognition emotion-recognition face-verification age-prediction gender-prediction deepid vgg-face deepface race-classification large-scale-face-recognition face-recognition-framework
  • Updated Jul 6, 2020
  • Python

richmondu / libfaceid

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libfaceid is a research framework for prototyping of face recognition solutions. It seamlessly integrates multiple detection, recognition and liveness models w/ speech synthesis and speech recognition.

raspberry-pi opencv machine-learning deep-learning tensorflow scikit-learn keras speech-synthesis speech-recognition face-recognition face-detection dlib openface facenet convolutional-neural-network pose-estimation emotion-detection gender-detection age-detection face-spoofing-detection
  • Updated Jan 28, 2020
  • Python

tamerthamoqa / facenet-pytorch-vggface2

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A PyTorch implementation of Google's FaceNet [1] paper for training a facial recognition model with Triplet Loss and an implementation of the Shenzhen Institutes of Advanced Technology's 'Center Loss' [2] combined with Cross Entropy Loss using the VGGFace2 dataset. A pre-trained model using Triplet Loss is available for download.

pytorch face-recognition facenet multi-gpu triplet-loss center-loss lfw-dataset cross-entropy-loss pretrained-model vggface2-dataset
  • Updated Jul 3, 2020
  • Python

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