4 research outputs found
Octuplet Loss: Make Face Recognition Robust to Image Resolution
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
Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021
As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity