A coolection of tools for organizing directories, specifically converting the Labeled Faces of the Wild (cropped) to a common standard.

An image recognition process contained in the LFW database http://vis-www.cs.umass.edu/lfw/#download is carried out with extreme simplicity, taking advantage of the ease of sklearn to implement the SVM model. Cascading face recognition is also used to refine the images, obtaining accuracy greater than 70% in the test with images that do not appe…

https://www.dunxuan.xyz/experience/LFW-VOC-Label.html

Train/validate VGGface2 dataset based on L2-constrained softmax loss.

Face Recognition with convolutional neural network (CNN) on Labeled Faces in the Wild (LFW) dataset

Face recognition in PyTorch.

Multi-metric-learning-discriminative-for-face-verification-SPDML-with-Labled-FACE-In Wild-(LFW ) dataset-YFT

Pytorch implementation of "A Better Autoencoder for Image: Convolutional Autoencoder" by Yifei Zhang

Face Recognition using FaceNet

Tensorflow implementation of dcgan

Deep Siamese network for low-resolution face recognition (2021, APSIPA ASC)

Introduction of how to use LFW database according to its protocols

128D Facenet Embedding Visualisation

Face recognition

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.

Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination (2019, ICIP)