Exploring factors for improving low resolution face recognition

Omid Abdollahi Aghdam, Behzad Bozorgtabar, Hazim Kemal Ekenel, Jean Philippe Thiran

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

19 Atıf (Scopus)

Özet

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-1M 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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
YayınlayanIEEE Computer Society
Sayfalar2363-2370
Sayfa sayısı8
ISBN (Elektronik)9781728125060
DOI'lar
Yayın durumuYayınlandı - Haz 2019
Etkinlik32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Süre: 16 Haz 201920 Haz 2019

Yayın serisi

AdıIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Hacim2019-June
ISSN (Basılı)2160-7508
ISSN (Elektronik)2160-7516

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???event.eventtypes.event.conference???32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Ülke/BölgeUnited States
ŞehirLong Beach
Periyot16/06/1920/06/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

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