Smart Image Segregation using Face Recognition

EasyChair Preprint 5344

Abstract

This paper discusses a method of classifying and segregating images using facial recognition. This study would define the most critical features for evaluating within a model that can distinguish one face from another. Extraction and collection of features are critical measures to better distinguish people from one another. Extraction of features is the method of extracting different properties from a set of results. Selection of features is the method that follows extraction, where the most important features are chosen to represent each sample. Once the appropriate features are selected, they are added as potential inputs to the neural network. The image dataset used for this project has been provided to me by a relative. This dataset contains suitable .jpg files containing over 4000 images we aim to classify and segregate. The model in this research differentiates between types of people based on their faces. Diverse features are tested to determine which elements performed better. The project mainly uses dlib and face recognition libraries in order to provide the functionality

Keyphrases: face recognition, image classification, image segmentation

@booklet{EasyChair:5344,
  author    = {Raj Shaiwalla and Arindam Chaudhuri},
  title     = {Smart Image Segregation using Face Recognition},
  howpublished = {EasyChair Preprint 5344},
  year      = {EasyChair, 2021}}