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.

Porting pytorch dcgan on FloydHub

A PyTorch Implementation of ShuffleFaceNet.

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

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

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 Implementation using PCA, eigenfaces, and SVM

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

Image Inpainting using Context Encoders

🎨 Restore images flawlessly with our Image Inpainting Showcase! Using Convolutional Neural Networks, we revive corrupted parts. Explore code, models, and innovation for visual perfection. Join the AI journey of artistry and magic! 🖼️🔮 #ImageInpainting

Analysing different dimensionality reduction techniques and svm

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

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

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

Facial Images Generation With Generative Adversarial Network (GAN)

A simple colab notebook on validation of face recognition model on LFW test dataset.

Autoencoders test from Coursera's Advanced Machine Learning - Intro to Deep Learning course.

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

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.