Mask R-CNN ile Derin Yüz Sezici Gerçekleme

Translated title of the contribution: Design of a deep face detector by mask R-CNN

Ozan Cakiroglu, Caner Ozer, Bilge Gunsel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

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.

Translated title of the contributionDesign of a deep face detector by mask R-CNN
Original languageTurkish
Title of host publication27th Signal Processing and Communications Applications Conference, SIU 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119045
DOIs
Publication statusPublished - Apr 2019
Event27th Signal Processing and Communications Applications Conference, SIU 2019 - Sivas, Turkey
Duration: 24 Apr 201926 Apr 2019

Publication series

Name27th Signal Processing and Communications Applications Conference, SIU 2019

Conference

Conference27th Signal Processing and Communications Applications Conference, SIU 2019
Country/TerritoryTurkey
CitySivas
Period24/04/1926/04/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

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