Authors :
Deeksha Patel; Jyostna Parasabaktula; Shiv Mangal Yadav; Ritendu Bhattacharyya; Bharani Kumar Depuru
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/3283erns
Scribd :
http://tinyurl.com/2ev64rte
DOI :
https://doi.org/10.5281/zenodo.10453298
Abstract :
Face recognition is a concept of the safest way
of logging on; it entails that our facial images are
acquired, detected, and subsequently authenticated by
the particular interface. In this present digital
generation, safe authentication of the interfaces is the
primary cautionary aspect that should be maintained,
and this model suggests a secure and strong
authentication system. This paper recommends a face
recognition login interface that involves deep learning
models to provide a strong and secure authentication
mechanism. It involves the extraction of facial images,
proposes a solution to enhance accuracy and
trustworthiness, and presents a weighty improvement
over the traditional username-password login method.
This gives us a user-friendly login experience along with
the highest level of security. These facial authentication
models are being used in numerous fields, ranging from
security, healthcare, marketing, retail, public events,
payments, door unlocking and video monitoring systems,
user authentication on devices, etc. It is also useful for
multi-class classification problems. This paper includes
face recognition techniques from convolutional neural
networks (CNN) and transformer models like ViT
(vision transformer), VGG16, RestNet50, Inception V3,
and EfficientNetB0. It proposes that the best model will
be deployed using Streamlit.
Keywords :
Secured Authentication System, Facial Recognition, Deep Learning, Vision Transformer, VGG16, Image Classification, Streamlit.
Face recognition is a concept of the safest way
of logging on; it entails that our facial images are
acquired, detected, and subsequently authenticated by
the particular interface. In this present digital
generation, safe authentication of the interfaces is the
primary cautionary aspect that should be maintained,
and this model suggests a secure and strong
authentication system. This paper recommends a face
recognition login interface that involves deep learning
models to provide a strong and secure authentication
mechanism. It involves the extraction of facial images,
proposes a solution to enhance accuracy and
trustworthiness, and presents a weighty improvement
over the traditional username-password login method.
This gives us a user-friendly login experience along with
the highest level of security. These facial authentication
models are being used in numerous fields, ranging from
security, healthcare, marketing, retail, public events,
payments, door unlocking and video monitoring systems,
user authentication on devices, etc. It is also useful for
multi-class classification problems. This paper includes
face recognition techniques from convolutional neural
networks (CNN) and transformer models like ViT
(vision transformer), VGG16, RestNet50, Inception V3,
and EfficientNetB0. It proposes that the best model will
be deployed using Streamlit.
Keywords :
Secured Authentication System, Facial Recognition, Deep Learning, Vision Transformer, VGG16, Image Classification, Streamlit.
