Facial Expressions as a Vulnerability in Face Recognition

Peña, Alejandro; Serna, Ignacio; Morales, Aythami; Fiérrez, Julián; Lapedriza, Àgata

doi:10.48550/arxiv.2011.08809

Cited by 2 publications

(2 citation statements)

References 61 publications

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“…The main factors that affect FR include (i) pose [8]- [11], (ii) illumination [12]- [14], (iii) facial expression [15]- [17], (iv) age [18], [19], (v) gender variations [20], [21] and, (vi) facial occlusions [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Toward Robust Facial Authentication for Low-Power Edge-AI Consumer Devices

et al. 2022

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Robust authentication for low-power consumer devices without a keyboard remains a challenge. The recent availability of low-power neural accelerator hardware, combined with improvements in neural facial recognition algorithms provides enabling technology for low-power, on-device facial authentication. The present research work explores a number of approaches to test the robustness of a state-of-the-art facial recognition (FR) technique, Arcface for such end-to-end applications. As extreme lighting conditions and facial pose are the two more challenging scenarios for FR we focus on these. Due to the general lack of large-scale multiple-identity datasets, GAN-based re-lighting and pose techniques are used to explore the effects on FR performance. These results are further validated on the best available multi-identity datasets -MultiPIE and BIWI. The results show that FR is quite robust to pose variations up to 45-55 degrees, but the outcomes are not definitive for the tested lighting scenarios. For lighting, the tested GAN-based relighting augmentations show significant effects on FR robustness. However, the lighting scenarios from MultiPIE dataset -the best available public dataset -show some conflicting results. It is unclear if this is due to an incorrectly learned GAN relighting transformation or, alternatively, to mixed ambient/directional lighting scenes in the dataset. However, it is shown that the GAN-induced FR errors for extreme lighting conditions can be corrected by fine-tuning the FR network layers. The conclusions support the feasibility of implementing a robust authentication method for low-power consumer devices.

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Section: Introductionmentioning

confidence: 99%

Toward Robust Facial Authentication for Low-Power Edge-AI Consumer Devices

et al. 2022

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“…The main factors that affect FR include (i) pose [37,47,3,18], (ii) illumination [2,49,42], (iii) facial expression [29,30,28], (iv) age [31,6] and, (v) gender variations [44,27].…”

Section: Introductionmentioning

confidence: 99%

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

Varkarakis,

Yao,

Corcoran

2021

Preprint

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The recent availability of low-power neural accelerator hardware, combined with improvements in end-to-end neural facial recognition algorithms provides enabling technology for on-device facial authentication. The present research work examines the effects of directional lighting on a State-of-Art (SoA) neural face recognizer. A synthetic re-lighting technique is used to augment data samples due to the lack of public data-sets with sufficient directional lighting variations. Top lighting and its variants (top-left, topright) are found to have minimal effect on accuracy, while bottom-left or bottom-right directional lighting have the most pronounced effects. Following the fine-tuning of network weights, the face recognition model is shown to achieve close to the original Receiver Operating Characteristic curve (ROC) performance across all lighting conditions, and demonstrates an ability to generalize beyond the lighting augmentations used in the fine-tuning dataset. This work shows that a SoA neural face recognition models can be tuned to compensate for directional lighting effects, removing the need for a pre-processing step prior to applying facial recognition.

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Facial Expressions as a Vulnerability in Face Recognition

Cited by 2 publications

References 61 publications

Toward Robust Facial Authentication for Low-Power Edge-AI Consumer Devices

Toward Robust Facial Authentication for Low-Power Edge-AI Consumer Devices

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

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