Exploring Factors for Improving Low Resolution Face Recognition

State-of-the-art deep face recognition approaches report near perfect performance on popular benchmarks, e.g., Labeled Faces in the Wild. However, their performance deteriorates significantly when they are applied on low quality images, such as those acquired by surveillance cameras. A further challenge for low resolution face recognition for surveillance applications is the matching of recorded low resolution probe face images with high resolution reference images, which could be the case in watchlist scenarios. In this paper, we have addressed these problems and investigated the factors that would contribute to the identification performance of the state-of-the-art deep face recognition models when they are applied to low resolution face recognition under mismatched conditions. We have observed that the following factors affect performance in a positive way: appearance variety and resolution distribution of the training dataset, resolution matching between the gallery and probe images, and the amount of information included in the probe images. By leveraging this information, we have utilized deep face models trained on MS-Celeb-IM and fine-tuned on VGGFace2 dataset and achieved state-of-the-art accuracies on the SCFace and ICB-RW benchmarks, even without using any training data from the datasets of these benchmarks.


Published in:
2019 Ieee/Cvf Conference On Computer Vision And Pattern Recognition Workshops (Cvprw 2019), 2363-2370
Presented at:
32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, Jun 16-20, 2019
Year:
Jan 01 2019
Publisher:
New York, IEEE
ISSN:
2160-7508
ISBN:
978-1-7281-2506-0
Laboratories:




 Record created 2020-10-02, last modified 2020-10-25


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