Code for training and parameter tuning of a machine learning model for non-linear aggregation of image denoising estimators using COBRA combined regression strategy. The face images used for training and testing are taken from the Labelled Faces in the Wild (LFW) dataset.

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

Simple application of VGG16 for the recognition of images, obtained from LFW, of a limited number of famous(15) with good performance (greater than 80%)

A recognition process of images contained in the LFW database http://vis-www.cs.umass.edu/lfw/#download is carried out using two models, one based on the minimum distance between training image records and test and another that is an adaptation of the CNN KERAS model https://keras.io/examples/vision/mnist_convnet/. Both models are complementary.…

Face Recognition in Caffe using different VGGNet architectures on ColorFeret and LFW datasets

Fast semi-supervised face recognition model using graph theory and fast computer vision methods.

Image Inpainting using Context Encoders

PyTorch implementation of LS-CNN: Characterizing Local Patches at Multiple Scales for Face Recognition

PcaNet, PCA Network, Deep Learning, Face Classification, LFW dataset, SVM

Face Recognition with SVM classifier using PCA, ICA, NMF, LDA reduced face vectors

A PyTorch Implementation of ShuffleFaceNet.

Porting pytorch dcgan on FloydHub

A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.