A Facial Recognition System using Python, OpenCV, Dlib. This project includes data preprocessing, face detection, feature extraction, and model training. Explore the LFW dataset, train a Support Vector Classifier, and implement real-time face recognition. Comprehensive notebooks and scripts guide each step.

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

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ML model for grouping similar faces using cutting-edge deep learning and computer vision techniques. Custom dataset of 300 images captures comprehensive facial variations. Siamese network outperforms Face-Net, delivering reliable clustering results.

Implementation and training of facial identification model on LFW dataset.

Just some implementations of my understanding of Generative models

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%)

GAN trained on the preprocessed LFW dataset with an analysis of peculiar generation artifacts.

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

dog breed classifier based on convnets, using catdog and lfw datasets.

A PyTorch Implementation of ShuffleFaceNet.

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

Final Project Of Computational Intelligence - Fall 2021 - LightGBM, RandomForest and StackingClassifier

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.

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.

Facial Images Generation With Generative Adversarial Network (GAN)

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

Face Recognition Implementation using PCA, eigenfaces, and SVM

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