Does a Face Mask Protect My Privacy?: Deep Learning to Predict Protected Attributes from Masked Face Images

Seneviratne, Sachith; Kasthuriarachchi, Nuran; Rasnayaka, Sanka; Hettiachchi, Danula; Shariffdeen, Ridwan

doi:10.1007/978-3-030-97546-3_8

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…These findings led to a new direction in the FR research. Hence, an additional effort was made to solve the masked face recognition (MFR) problem, which benefit from contributions in the form of case-study papers [17,16,36,34], methods [28,25,29,4] and competitions [5,11]. Moreover, the effect of masks were not exclusively linked to recognition and studies can be found on their effect on face presentation attack detection methods [15,16].…”

Section: Introductionmentioning

confidence: 99%

OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions

Neto¹,

Pinto²,

Damer³

et al. 2022

Preprint

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This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022). OCFR-2022 attracted a total of 3 participating teams, from academia. Eventually, six valid submissions were submitted and then evaluated by the organizers. The competition was held to address the challenge of face recognition in the presence of severe face occlusions. The participants were free to use any training data and the testing data was built by the organisers by synthetically occluding parts of the face images using a well-known dataset. The submitted solutions presented innovations and performed very competitively with the considered baseline. A major output of this competition is a challenging, realistic, and diverse, and publicly available occluded face recognition benchmark with well defined evaluation protocols.

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