Mask R-CNN ile Derin Yüz Sezici Gerçekleme

Ozan Cakiroglu, Caner Ozer, Bilge Gunsel

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

10 Atıf (Scopus)

Özet

In this work an existing object detector, Mask RCNN, is trained for face detection and performance results are reported by using the learned model. Differing from the existing work, it is aimed to train the deep detector with a small number of training examples and also to perform instance segmentation along with an object bounding box detection. Training set includes 2695 face examples collected from PASCAL-VOC database. Performance has been reported on 159,000 test faces of WIDER FACE benchmarking database. Numerical results demonstrate that the trained Mask R-CNN provides higher detection rates with respect to the baseline detector [1], particularly 6%, 12%, and 3% higher face detection accuracy for the small, medium and large scale faces, respectively. It is also reported that our performance outperforms Viola Jones face detector. We released the face segmentation ground-truth data that was used to train Mask R-CNN and training-test routines developed in TensorFlow platform to public usage at our GitHub repository.

Tercüme edilen katkı başlığıDesign of a deep face detector by mask R-CNN
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı27th Signal Processing and Communications Applications Conference, SIU 2019
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781728119045
DOI'lar
Yayın durumuYayınlandı - Nis 2019
Etkinlik27th Signal Processing and Communications Applications Conference, SIU 2019 - Sivas, Turkey
Süre: 24 Nis 201926 Nis 2019

Yayın serisi

Adı27th Signal Processing and Communications Applications Conference, SIU 2019

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???event.eventtypes.event.conference???27th Signal Processing and Communications Applications Conference, SIU 2019
Ülke/BölgeTurkey
ŞehirSivas
Periyot24/04/1926/04/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Convolutional neural networks
  • Deep learning
  • Face detection
  • Instance segmentation

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