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Convolutional and Recurrent Neural Networks for Face Image Analysis


Year of publication


Published in

Foundations of Computing and Decision Sciences

Journal year: 2019 | Journal volume: vol. 44 | Journal number: no. 3

Article type

scientific article

Publication language


  • deep learning
  • convolutional neural networks
  • recurrent neural net-works
  • facial landmark localization
  • facial parts detection
  • computer vision
  • image processing

EN In the presented research two Deep Neural Network (DNN) models for face image analysis were developed. The first one detects eyes, nose and mouth and it is based on a moderate size Convolutional Neural Network (CNN) while the second one identifies 68 landmarks resulting in a novel Face Alignment Network composed of a CNN and a recurrent neural network. The Face Parts Detector inputs face image and outputs the pixel coordinates of bounding boxes for detected facial parts. The Face Alignment Network extracts deep features in CNN module while in the recurrent module it generates 68 facial landmarks using not only this deep features, but also the geometry of facial parts. Both methods are robust to varying head poses and changing light conditions.

Pages (from - to)

331 - 347




License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

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