Design of a Deep Face Detector by Mask R-CNN


Cakiroglu O., Özer C., Günsel B.

27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey, 24 - 26 April 2019 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2019.8806447
  • City: Sivas
  • Country: Turkey
  • Keywords: Face detection, instance segmentation, deep learning, convolutional neural networks
  • Istanbul Technical University Affiliated: Yes

Abstract

In this work an existing object detector, Mask R-CNN, 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.