The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study

• URL: http://arxiv.org/abs/2007.13521v2
• Date: Thu, 20 Aug 2020 18:57:25 GMT
• Title: The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study
• Authors: Naser Damer, Jonas Henry Grebe, Cong Chen, Fadi Boutros, Florian Kirchbuchner and Arjan Kuijper
• Abstract summary: Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied.
• Score: 17.577672647262116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases. We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.

Related papers

• Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition [47.248075664420874]
  Current general face recognition system suffers from serious performance degradation when encountering occluded scenes. This paper proposes a Multi-task gEnerative mask dEcoupling face Recognition (MEER) network to jointly handle these two tasks. We first present a novel mask decoupling module to disentangle mask and identity information, which makes the network obtain purer identity features from visible facial components.
  arXiv  Detail & Related papers  (2023-11-20T03:23:03Z)
• Identity masking effectiveness and gesture recognition: Effects of eye enhancement in seeing through the mask [0.0]
  Face identity masking algorithms are designed to interfere with identification, while preserving information about facial actions. We evaluated the effectiveness of identity-masking algorithms based on Canny filters, applied with and without eye enhancement. We conclude that relatively simple, filter-based masking algorithms can be used in privacy protection without compromising action perception.
  arXiv  Detail & Related papers  (2023-01-20T03:10:19Z)
• Face Presentation Attack Detection [59.05779913403134]
  Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios.
  arXiv  Detail & Related papers  (2022-12-07T14:51:17Z)
• Mask-invariant Face Recognition through Template-level Knowledge Distillation [3.727773051465455]
  Masks affect the performance of previous face recognition systems. We propose a mask-invariant face recognition solution (MaskInv) In addition to the distilled knowledge, the student network benefits from additional guidance by margin-based identity classification loss.
  arXiv  Detail & Related papers  (2021-12-10T16:19:28Z)
• Adversarial Mask: Real-World Adversarial Attack Against Face Recognition Models [66.07662074148142]
  We propose a physical adversarial universal perturbation (UAP) against state-of-the-art deep learning-based facial recognition models. In our experiments, we examined the transferability of our adversarial mask to a wide range of deep learning models and datasets. We validated our adversarial mask effectiveness in real-world experiments by printing the adversarial pattern on a fabric medical face mask.
  arXiv  Detail & Related papers  (2021-11-21T08:13:21Z)
• I Only Have Eyes for You: The Impact of Masks On Convolutional-Based Facial Expression Recognition [78.07239208222599]
  We evaluate how the recently proposed FaceChannel adapts towards recognizing facial expressions from persons with masks. We also perform specific feature-level visualization to demonstrate how the inherent capabilities of the FaceChannel to learn and combine facial features change when in a constrained social interaction scenario.
  arXiv  Detail & Related papers  (2021-04-16T20:03:30Z)
• Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection [103.7264459186552]
  Face presentation attack detection (PAD) is essential to secure face recognition systems. Most existing 3D mask PAD benchmarks suffer from several drawbacks. We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
  arXiv  Detail & Related papers  (2021-04-13T12:48:38Z)
• Masked Face Recognition: Human vs. Machine [6.24950085812444]
  The recent COVID-19 pandemic has increased the focus on hygienic and contactless identity verification methods. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. This work provides a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic face recognition solutions.
  arXiv  Detail & Related papers  (2021-03-02T18:36:01Z)
• Unmasking Face Embeddings by Self-restrained Triplet Loss for Accurate Masked Face Recognition [6.865656740940772]
  We present a solution to improve the masked face recognition performance. Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models. We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities.
  arXiv  Detail & Related papers  (2021-03-02T13:43:11Z)
• Facial Expressions as a Vulnerability in Face Recognition [73.85525896663371]
  This work explores facial expression bias as a security vulnerability of face recognition systems. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies.
  arXiv  Detail & Related papers  (2020-11-17T18:12:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.