M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis
Recent regulations to block the widespread transmission of COVID-19 disease among people impose the use of facial masks indoor and outdoor. Such restriction becomes critical in all those scenarios where access controls take benefit from biometric recognition systems. The occlusions due to the presen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9874825/ |
| _version_ | 1811268203680104448 |
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| author | Lucia Cimmino Michele Nappi Fabio Narducci Chiara Pero |
| author_facet | Lucia Cimmino Michele Nappi Fabio Narducci Chiara Pero |
| author_sort | Lucia Cimmino |
| collection | DOAJ |
| description | Recent regulations to block the widespread transmission of COVID-19 disease among people impose the use of facial masks indoor and outdoor. Such restriction becomes critical in all those scenarios where access controls take benefit from biometric recognition systems. The occlusions due to the presence of a facial mask make a significant portion of human faces unavailable for feature extraction and analysis. This work explores the contribution of the solely periocular region of the face to achieve a robust recognition approach suitable for mobile devices. Rather than working on a static analysis of the facial features, like largely done by work on periocular recognition in the literature, the proposed study focuses the attention on the analysis of face dynamics so that the spatio-temporal features make the recogniser frame-independent and tolerant to user movements during the acquisition. To obtain a lightweight processing, which is compliant with limited computing power of mobile devices, the spatio-temporal representation of the periocular region has analysed and classified through Machine Learning approaches. The experimental discussion has been performed on a new dataset, Mobile Masked Face REcognition Database, specifically designed to analyse the periocular region dynamics in presence of facial masks. For a wider comparative analysis, a publicly available dataset called XM2VTS has been considered as well as Deep Learning solutions have been experimented to discuss the challenging aspects of the recognition problem. Moreover, a summary of the state-of-the-art on periocular recognition driven by COVID pandemic has been presented, showing how the research efforts in this field focused on recognition of still images. Experimental results show promising levels of performance as well as limitations of the proposed approach, creating the premises for future directions. |
| first_indexed | 2024-04-12T21:17:32Z |
| format | Article |
| id | doaj.art-e286680c54164737b712fc1f77d4b314 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T21:17:32Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e286680c54164737b712fc1f77d4b3142022-12-22T03:16:24ZengIEEEIEEE Access2169-35362022-01-0110943889440210.1109/ACCESS.2022.32035799874825M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics AnalysisLucia Cimmino0https://orcid.org/0000-0003-4880-4975Michele Nappi1https://orcid.org/0000-0002-2517-2867Fabio Narducci2https://orcid.org/0000-0003-4879-7138Chiara Pero3https://orcid.org/0000-0002-5517-2198Department of Computer Science, University of Salerno, Salerno, ItalyDepartment of Computer Science, University of Salerno, Salerno, ItalyDepartment of Computer Science, University of Salerno, Salerno, ItalyDepartment of Computer Science, University of Salerno, Salerno, ItalyRecent regulations to block the widespread transmission of COVID-19 disease among people impose the use of facial masks indoor and outdoor. Such restriction becomes critical in all those scenarios where access controls take benefit from biometric recognition systems. The occlusions due to the presence of a facial mask make a significant portion of human faces unavailable for feature extraction and analysis. This work explores the contribution of the solely periocular region of the face to achieve a robust recognition approach suitable for mobile devices. Rather than working on a static analysis of the facial features, like largely done by work on periocular recognition in the literature, the proposed study focuses the attention on the analysis of face dynamics so that the spatio-temporal features make the recogniser frame-independent and tolerant to user movements during the acquisition. To obtain a lightweight processing, which is compliant with limited computing power of mobile devices, the spatio-temporal representation of the periocular region has analysed and classified through Machine Learning approaches. The experimental discussion has been performed on a new dataset, Mobile Masked Face REcognition Database, specifically designed to analyse the periocular region dynamics in presence of facial masks. For a wider comparative analysis, a publicly available dataset called XM2VTS has been considered as well as Deep Learning solutions have been experimented to discuss the challenging aspects of the recognition problem. Moreover, a summary of the state-of-the-art on periocular recognition driven by COVID pandemic has been presented, showing how the research efforts in this field focused on recognition of still images. Experimental results show promising levels of performance as well as limitations of the proposed approach, creating the premises for future directions.https://ieeexplore.ieee.org/document/9874825/Periocular analysisfeature extractiontemporal feature representationmachine learningbiometric recognition |
| spellingShingle | Lucia Cimmino Michele Nappi Fabio Narducci Chiara Pero M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis IEEE Access Periocular analysis feature extraction temporal feature representation machine learning biometric recognition |
| title | M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis |
| title_full | M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis |
| title_fullStr | M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis |
| title_full_unstemmed | M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis |
| title_short | M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis |
| title_sort | m2fred mobile masked face recognition through periocular dynamics analysis |
| topic | Periocular analysis feature extraction temporal feature representation machine learning biometric recognition |
| url | https://ieeexplore.ieee.org/document/9874825/ |
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