Application of Facial Recognition using Convolutional Neural Networks
for Entry Access Control
- URL: http://arxiv.org/abs/2011.11257v1
- Date: Mon, 23 Nov 2020 07:55:24 GMT
- Title: Application of Facial Recognition using Convolutional Neural Networks
for Entry Access Control
- Authors: Lars Lien Ankile, Morgan Feet Heggland, Kjartan Krange
- Abstract summary: The paper focuses on solving the supervised classification problem of taking images of people as input and classifying the person in the image as one of the authors or not.
Two approaches are proposed: (1) building and training a neural network called WoodNet from scratch and (2) leveraging transfer learning by utilizing a network pre-trained on the ImageNet database.
The results are two models classifying the individuals in the dataset with high accuracy, achieving over 99% accuracy on held-out test data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The purpose of this paper is to design a solution to the problem of facial
recognition by use of convolutional neural networks, with the intention of
applying the solution in a camera-based home-entry access control system. More
specifically, the paper focuses on solving the supervised classification
problem of taking images of people as input and classifying the person in the
image as one of the authors or not. Two approaches are proposed: (1) building
and training a neural network called WoodNet from scratch and (2) leveraging
transfer learning by utilizing a network pre-trained on the ImageNet database
and adapting it to this project's data and classes. In order to train the
models to recognize the authors, a dataset containing more than 150 000 images
has been created, balanced over the authors and others. Image extraction from
videos and image augmentation techniques were instrumental for dataset
creation. The results are two models classifying the individuals in the dataset
with high accuracy, achieving over 99% accuracy on held-out test data. The
pre-trained model fitted significantly faster than WoodNet, and seems to
generalize better. However, these results come with a few caveats. Because of
the way the dataset was compiled, as well as the high accuracy, one has reason
to believe the models over-fitted to the data to some degree. An added
consequence of the data compilation method is that the test dataset may not be
sufficiently different from the training data, limiting its ability to validate
generalization of the models. However, utilizing the models in a web-cam based
system, classifying faces in real-time, shows promising results and indicates
that the models generalized fairly well for at least some of the classes (see
the accompanying video).
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