Advanced search
Start date
Betweenand
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Two-tiered face verification with low-memory footprint for mobile devices

Full text
Author(s):
Padilha, Rafael [1] ; Andalo, Fernanda A. [1] ; Bertocco, Gabriel [1] ; Almeida, Waldir R. [1] ; Dias, William [1] ; Resek, Thiago [2] ; Torres, Ricardo da S. [3] ; Wainer, Jacques [1] ; Rocha, Anderson [1]
Total Authors: 9
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[2] Motorola Mobil LLC, Campinas, SP - Brazil
[3] NTNU, Fac Informat Technol & Elect Engn, Dept ICT & Nat Sci, Alesund - Norway
Total Affiliations: 3
Document type: Journal article
Source: IET BIOMETRICS; v. 9, n. 5, p. 205-215, SEP 2020.
Web of Science Citations: 0
Abstract

Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication. In this work, the authors propose a facial verification method optimised to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features and a convolutional neural network (CNN) to verify if the person depicted in a photograph corresponds to the device owner. To train a CNN for the verification task, the authors propose a hybrid-image input, which allows the network to process encoded information of a pair of face images. The proposed experiments show that the solution outperforms state of the art face verification methods, providing a 4x speedup when processing an image in recent smartphone models. Additionally, the authors show that the two-tiered procedure can be coupled with existing face verification CNNs improving their accuracy and efficiency. They also present a new data set of selfie pictures - RECOD Selfie data set - that hopefully will support future research in this scenario. (AU)

FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
Grantee:Anderson de Rezende Rocha
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/21957-2 - Learning Visual Clues of the Passage of Time
Grantee:Rafael Soares Padilha
Support type: Scholarships in Brazil - Doctorate