Evaluation of Local Texture Descriptors for Eyebrow-Based Continuous Mobile User Authentication

Mohammad, Ahmad Saeed; Rattani, Ajita; Derakhshani, Reza

doi:10.1007/978-3-030-38752-5_11

Cited by 1 publication

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“…Due to its lower uniqueness and permanence, the eyebrow is often considered a soft biometric [12] which is not suitable as a stand‐alone solution. Nonetheless, given their visibility in the presence of flu masks, resilience against many ocular occlusions, and easy ‘selfie’ like capture process, eyebrows can be a viable solution, and at times, the only option for short‐term or continuous user authentication through front‐facing cameras [13]. In this way, the eyebrow would not serve to completely replace the primary biometric modality, but rather complement it as an alternative when other selfie modalities are unavailable or weakened by issues such as makeup, face masks, reflections on eyeglasses, gazing, or closed eyes.…”

Section: Introductionmentioning

confidence: 99%

Subject independent evaluation of eyebrows as a stand‐alone biometric

2021

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Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from 'selfie' captures for personal identification. A less-visited ocular neighbourhood is the eyebrows region. Previous works on eyebrows have been mostly using subject-specific verification protocols where identities overlap between the training and test sets and do not directly address the issue of doppelgangers and twins. Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow-based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. Also a challenging simulated identical twins scenario in our training and testing data sets is presented. Our results show a 4.2% equal error rate (EER) and 0.993 area under the curve (AUC) on a subset of the FACES data set (154 subjects) and 6.8% EER, 0.978 AUC on a subset of the VISOB data set (350 subjects). Despite achieving promising accuracy during short-term evaluations, the proposed methods showed lower accuracy when matching against long-term data or when facing data captured under varying illumination and facial expressions.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Introductionmentioning

confidence: 99%