<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Convolutional and Recurrent Neural Networks for Face Image Analysis

Institute of Radioelectronics and Multimedia Technology

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Convolutional and Recurrent Neural Networks for Face Image Analysis"}

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...

Convolutional and Recurrent Neural Networks for Face Image Analysis

Online veröffentlicht: 28. Aug. 2019

Seitenbereich: 331 - 347

Eingereicht: 29. Jan. 2019

Akzeptiert: 23. Apr. 2019

DOI: https://doi.org/10.2478/fcds-2019-0017

Schlüsselwörter
deep learning, convolutional neural networks, recurrent neural networks, facial landmark localization, facial parts detection, computer vision, image processing

© 2019 Kıvanç Yüksel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Convolutional and Recurrent Neural Networks for Face Image Analysis

Online veröffentlicht: 28. Aug. 2019

Seitenbereich: 331 - 347

Eingereicht: 29. Jan. 2019

Akzeptiert: 23. Apr. 2019

DOI: https://doi.org/10.2478/fcds-2019-0017

Schlüsselwörterdeep learning, convolutional neural networks, recurrent neural networks, facial landmark localization, facial parts detection, computer vision, image processing

© 2019 Kıvanç Yüksel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
deep learning, convolutional neural networks, recurrent neural networks, facial landmark localization, facial parts detection, computer vision, image processing