Octuplet Loss: Make Face Recognition Robust to Image Resolution

• URL: http://arxiv.org/abs/2207.06726v2
• Date: Tue, 21 Mar 2023 07:23:13 GMT
• Title: Octuplet Loss: Make Face Recognition Robust to Image Resolution
• Authors: Martin Knoche, Mohamed Elkadeem, Stefan H\"ormann, Gerhard Rigoll
• Abstract summary: We propose a novel combination of the popular triplet loss to improve robustness against image resolution. We leverage the relationship between high-resolution images and their synthetically down-sampled variants jointly with their identity labels. Fine-tuning several state-of-the-art approaches with our method proves that we can significantly boost performance for cross-resolution (high-to-low resolution) face verification.
• Score: 5.257115841810258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image resolution, or in general, image quality, plays an essential role in the performance of today's face recognition systems. To address this problem, we propose a novel combination of the popular triplet loss to improve robustness against image resolution via fine-tuning of existing face recognition models. With octuplet loss, we leverage the relationship between high-resolution images and their synthetically down-sampled variants jointly with their identity labels. Fine-tuning several state-of-the-art approaches with our method proves that we can significantly boost performance for cross-resolution (high-to-low resolution) face verification on various datasets without meaningfully exacerbating the performance on high-to-high resolution images. Our method applied on the FaceTransformer network achieves 95.12% face verification accuracy on the challenging XQLFW dataset while reaching 99.73% on the LFW database. Moreover, the low-to-low face verification accuracy benefits from our method. We release our code to allow seamless integration of the octuplet loss into existing frameworks.

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