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Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition

Neto, Pedro C. ; Pinto, Joao Ribeiro ; Boutros, Fadi ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S. (2022)
Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition.
In: IEEE Access, 10
doi: 10.1109/ACCESS.2022.3199014
Article, Bibliographie

Abstract

Over the years, the evolution of face recognition (FR) algorithms has been steep and accelerated by a myriad of factors. Motivated by the unexpected elements found in real-world scenarios, researchers have investigated and developed a number of methods for occluded face recognition (OFR). However, due to the SarS-Cov2 pandemic, masked face recognition (MFR) research branched from OFR and became a hot and urgent research challenge. Due to time and data constraints, these models followed different and novel approaches to handle lower face occlusions, i.e., face masks. Hence, this study aims to evaluate the different approaches followed for both MFR and OFR, find linked details about the two conceptually similar research directions and understand future directions for both topics. For this analysis, several occluded and face recognition algorithms from the literature are studied. First, they are evaluated in the task that they were trained on, but also on the other. These methods were picked accordingly to the novelty of their approach, proven state-of-the-art results, and publicly available source code. We present quantitative results on 4 occluded and 5 masked FR datasets, and a qualitative analysis of several MFR and OFR models on the Occ-LFW dataset. The analysis presented, sustain the interoperable deployability of MFR methods on OFR datasets, when the occlusions are of a reasonable size. Thus, solutions proposed for MFR can be effectively deployed for general OFR.

Item Type: Article
Erschienen: 2022
Creators: Neto, Pedro C. ; Pinto, Joao Ribeiro ; Boutros, Fadi ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S.
Type of entry: Bibliographie
Title: Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition
Language: English
Date: 2022
Journal or Publication Title: IEEE Access
Volume of the journal: 10
DOI: 10.1109/ACCESS.2022.3199014
URL / URN: https://doi.org/10.1109/ACCESS.2022.3199014
Abstract:

Over the years, the evolution of face recognition (FR) algorithms has been steep and accelerated by a myriad of factors. Motivated by the unexpected elements found in real-world scenarios, researchers have investigated and developed a number of methods for occluded face recognition (OFR). However, due to the SarS-Cov2 pandemic, masked face recognition (MFR) research branched from OFR and became a hot and urgent research challenge. Due to time and data constraints, these models followed different and novel approaches to handle lower face occlusions, i.e., face masks. Hence, this study aims to evaluate the different approaches followed for both MFR and OFR, find linked details about the two conceptually similar research directions and understand future directions for both topics. For this analysis, several occluded and face recognition algorithms from the literature are studied. First, they are evaluated in the task that they were trained on, but also on the other. These methods were picked accordingly to the novelty of their approach, proven state-of-the-art results, and publicly available source code. We present quantitative results on 4 occluded and 5 masked FR datasets, and a qualitative analysis of several MFR and OFR models on the Occ-LFW dataset. The analysis presented, sustain the interoperable deployability of MFR methods on OFR datasets, when the occlusions are of a reasonable size. Thus, solutions proposed for MFR can be effectively deployed for general OFR.

Uncontrolled Keywords: Biometrics, Machine learning, Face recognition, Deep learning
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 19 Sep 2022 13:39
Last Modified: 14 Jan 2024 12:29
PPN: 499513177
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