ABSTRACT
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. Author
ABSTRACT
Periocular recognition has attracted attention in recent times. The advent of the COVID-19 pandemic and the consequent obligation to wear facial masks made face recognition problematic due to the important occlusion of the lower part of the face. In this work, a dual-input Neural Network architecture is proposed. The structure is a Siamese-like model, with two identical parallel streams (called base models) that process the two inputs separately. The input is represented by RGB images of the right eye and the left eye belonging to the same subject. The outputs of the two base models are merged through a fusion layer. The aim is to investigate how deep feature aggregation affects periocular recognition. The experimentation is performed on the Masked Face Recognition Database (M 2 FRED) which includes videos of 46 participants with and without masks. Three different fusion layers are applied to understand which type of merging technique is most suitable for data aggregation. Experimental results show promising performance for almost all experimental configurations with a worst-case accuracy of 90% and a best-case accuracy of 97%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.