Implementation of Variational Auto-Encoder with TensorFlow 2.0
-
Updated
May 15, 2020 - Jupyter Notebook
Implementation of Variational Auto-Encoder with TensorFlow 2.0
Face recognition using PCA and some machine learning algorithm on Olivetti dataset.
The aim of this project is to classify the faces. Olivetti Faces dataset has been used. In this dataset there are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no …
Implemented SVC on the Olivetti dataset to predict if a person is wearing glasses or not by using cross-validation techniques in depth.
Training Siamese model to create a one-shot neural network model using Face-Net and MTCNN as the backbone. Yolov5 and OpenCV is used alongside to classify faces in the webcam feed
Classification of images (face images in Olivetti faces data set) using Logistic Regression and dimension reduction with PCA.
UNI S3: PCA training on OLLVETTI-FACES dataset / image size reduction.
Contains face completion using Linear Regression on olivetti face dataset
Face Recognition Algorithm using Unsupervised and Semi-supervised techniques using Olivetti faces dataset
Face recognition algorithm
Face Recognition Algorithm using Unsupervised and Semi-supervised techniques
Add a description, image, and links to the olivetti-dataset topic page so that developers can more easily learn about it.
To associate your repository with the olivetti-dataset topic, visit your repo's landing page and select "manage topics."