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

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]

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

• 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 Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/21957-2 - Learning Visual Clues of the Passage of Time
• Grantee: Rafael Soares Padilha
• Support Opportunities: Scholarships in Brazil - Doctorate