Pixel-Level Face Image Quality Assessment for Explainable Face Recognition

• URL: http://arxiv.org/abs/2110.11001v1
• Date: Thu, 21 Oct 2021 09:12:17 GMT
• Title: Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
• Authors: Philipp Terh\"orst, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper
• Abstract summary: We introduce the concept of pixel-level face image quality that determines the utility of pixels in a face image for recognition. Given an arbitrary face recognition network, in this work, we propose a training-free approach to assess the pixel-level qualities of a face image.
• Score: 5.858033242850427
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: An essential factor to achieve high performance in face recognition systems is the quality of its samples. Since these systems are involved in various daily life there is a strong need of making face recognition processes understandable for humans. In this work, we introduce the concept of pixel-level face image quality that determines the utility of pixels in a face image for recognition. Given an arbitrary face recognition network, in this work, we propose a training-free approach to assess the pixel-level qualities of a face image. To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model. Based on this model, quality-based gradients are back-propagated and converted into pixel-level quality estimates. In the experiments, we qualitatively and quantitatively investigated the meaningfulness of the pixel-level qualities based on real and artificial disturbances and by comparing the explanation maps on ICAO-incompliant faces. In all scenarios, the results demonstrate that the proposed solution produces meaningful pixel-level qualities. The code is publicly available.

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