Real-Time Semantic Segmentation in Mobile device

Deep Face Recognition in PyTorch

center loss for face recognition

Face Recognition in real-world images [ICASSP 2017]

Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)

Demo of Face Recognition web service

Neural networks for facial recognition using Keras and the LFW Face Database.

Some handy scripts for processing face datasets

This repo contains auto encoders and decoders using keras and tensor flow. It shows the exact encoding and decoding with the code part.

work in Advanced Topics in Multimedia Analysis and Indexing

Multivariate recurrent GANs for generating biomedical time-series given consistent dimensionality. Methodology involves drawing symmetries to adversarial image generation.

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.

This is the Python version of evaluation.m for <SphereFace: Deep Hypersphere Embedding for Face Recognition> in CVPR'17

Face recognition

face analysis project with tensorflow 2.0 || arcface

128D Facenet Embedding Visualisation

Tensorflow implementation of dcgan

A PyTorch Implementation of ShuffleFaceNet.

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

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

Introduction of how to use LFW database according to its protocols

Face Recognition using FaceNet

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