QMagFace: Simple and Accurate Quality-Aware Face Recognition

• URL: http://arxiv.org/abs/2111.13475v2
• Date: Tue, 30 Nov 2021 09:49:34 GMT
• Title: QMagFace: Simple and Accurate Quality-Aware Face Recognition
• Authors: Philipp Terh\"orst, Malte Ihlefeld, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper
• Abstract summary: We propose a simple and effective face recognition solution (QMag-Face) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss. The experiments conducted on several face recognition databases and benchmarks demonstrate that the introduced quality-awareness leads to consistent improvements in the recognition performance.
• Score: 5.5284501467256515
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Face recognition systems have to deal with large variabilities (such as different poses, illuminations, and expressions) that might lead to incorrect matching decisions. These variabilities can be measured in terms of face image quality which is defined over the utility of a sample for recognition. Previous works on face recognition either do not employ this valuable information or make use of non-inherently fit quality estimates. In this work, we propose a simple and effective face recognition solution (QMag-Face) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss. The proposed approach includes model-specific face image qualities in the comparison process to enhance the recognition performance under unconstrained circumstances. Exploiting the linearity between the qualities and their comparison scores induced by the utilized loss, our quality-aware comparison function is simple and highly generalizable. The experiments conducted on several face recognition databases and benchmarks demonstrate that the introduced quality-awareness leads to consistent improvements in the recognition performance. Moreover, the proposed QMagFace approach performs especially well under challenging circumstances, such as cross-pose, cross-age, or cross-quality. Consequently, it leads to state-of-the-art performances on several face recognition benchmarks, such as 98.50% on AgeDB, 83.95% on XQLFQ, and 98.74% on CFP-FP. The code for QMagFace is publicly available

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