Face Recognition in real-world images [ICASSP 2017]
-
Updated
Feb 27, 2017 - Python
Face Recognition in real-world images [ICASSP 2017]
A coolection of tools for organizing directories, specifically converting the Labeled Faces of the Wild (cropped) to a common standard.
Neural networks for facial recognition using Keras and the LFW Face Database.
This is the Python version of evaluation.m for <SphereFace: Deep Hypersphere Embedding for Face Recognition> in CVPR'17
work in Advanced Topics in Multimedia Analysis and Indexing
This project uses the Labeled Faces in the Wild (LFW) dataset, and the goal is to train variants of deep architectures to learn when a pair of images of faces is the same person or not. It is a pytorch implementation of Siamese network with 19 layers.
Introduction of how to use LFW database according to its protocols
128D Facenet Embedding Visualisation
Deep Face Recognition in PyTorch
This repo contains auto encoders and decoders using keras and tensor flow. It shows the exact encoding and decoding with the code part.
Some handy scripts for processing face datasets
Face recognition
Multi-metric-learning-discriminative-for-face-verification-SPDML-with-Labled-FACE-In Wild-(LFW ) dataset-YFT
Train/validate VGGface2 dataset based on L2-constrained softmax loss.
Face Recognition using FaceNet
center loss for face recognition
Official repository for MixFaceNets: Extremely Efficient Face Recognition Networks
Face Recognition with convolutional neural network (CNN) on Labeled Faces in the Wild (LFW) dataset
Add a description, image, and links to the lfw topic page so that developers can more easily learn about it.
To associate your repository with the lfw topic, visit your repo's landing page and select "manage topics."