Paper Info
Title
Distillation of an End-to-End Oracle for Face Verification and Recognition Sensors.
Abstract
Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.
Year
DOI
Venue
2020
10.3390/s20051369
SENSORS
Keywords
DocType
Volume
face recognition,face verification,biometric sensors,deep learning,distillation,convolutional neural networks,spatial transformer network
Journal
20
Issue
ISSN
Citations 
5
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Francesco Guzzi100.34
Luca De Bortoli200.34
Romina Soledad Molina300.34
Stefano Marsi415216.78
Sergio Carrato500.34
Giovanni Ramponi638151.84