polito.it
Politecnico di Torino (logo)

BiometricNet - A deep learning-based approach for biometric authentication

Stefano Brilli

BiometricNet - A deep learning-based approach for biometric authentication.

Rel. Enrico Magli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2020

[img]
Preview
PDF (Tesi_di_laurea) - Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Preview
Abstract:

This thesis presents an analysis of a novel, deep learning-based approach for user verification. Face verification is the task of comparing a candidate face to another and verifying whether it is a match. The traditional approach consists of relying on analytical metrics to shape the classification boundary. Instead of defining a metric, our approach allows the network to inherently learn it by mapping matching and non-matching face pairs onto different statistical distributions. Although any class of target distributions can be applied, using the Gaussians is a logical choice since the natural output of large enough fully-connected layers comes to be Gaussian. Moreover, since their masses tend to stay close to a single value, a threshold-based classification can be employed for the verification.

Relators: Enrico Magli
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 71
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/15331
Modify record (reserved for operators) Modify record (reserved for operators)