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

<p>An efficient face recognition system is essential for security and authentication-based applications. However, real-time face recognition systems have a few significant concerns, including face pose orientations. In the last decade, numerous solutions have been introduced to estimate distinct face pose orientations. Nevertheless, these solutions must be adequately addressed for the three main face pose orientations: Yaw, Pitch, and Roll. This paper proposed a novel deep transfer learning-based multitasking approach for solving three integrated tasks, i.e., face detection, landmarks detection, and face pose estimation. The face pose variation vulnerability has been intensely investigated here underlying three modules: image preprocessing, feature extraction module through deep transfer learning, and regression module for estimating the face poses. The experiments are performed on the well-known benchmark dataset Annotated Faces in the Wild (AFW). We evaluate the outcomes of the experiments to reveal that our proposed approach is superior to other recently available solutions.</p>
</abstract>

An efficient face recognition system is essential for security and authentication-based applications. However, real-time face recognition systems have a few significant concerns, including face pose orientations. In the last decade, numerous solutions have been introduced to estimate distinct face pose orientations. Nevertheless, these solutions must be adequately addressed for the three main face pose orientations: Yaw, Pitch, and Roll. This paper proposed a novel deep transfer learning-based multitasking approach for solving three integrated tasks, i.e., face detection, landmarks detection, and face pose estimation. The face pose variation vulnerability has been intensely investigated here underlying three modules: image preprocessing, feature extraction module through deep transfer learning, and regression module for estimating the face poses. The experiments are performed on the well-known benchmark dataset Annotated Faces in the Wild (AFW). We evaluate the outcomes of the experiments to reveal that our proposed approach is superior to other recently available solutions.

A Novel Deep Transfer Learning-Based Approach for Face Pose Estimation

Department of Computer Science and Engineering

Cybernetics and Information Technologies

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

{"article-title":"A Novel Deep Transfer Learning-Based Approach for Face Pose Estimation"}

An efficient face recognition system is essential for security and authentication-based applications. However, real-time face recognition systems have a few...

A Novel Deep Transfer Learning-Based Approach for Face Pose Estimation

Publié en ligne: 27 juin 2024

Pages: 105 - 121

Reçu: 30 mai 2023

Accepté: 21 avr. 2024

DOI: https://doi.org/10.2478/cait-2024-0018

Mots clés
Face alignment, Biometrics, Face recognition, Image processing, Landmark detection, Deep convolutional neural network

© 2024 Mayank Kumar Rusia et al., published by Sciendo

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

A Novel Deep Transfer Learning-Based Approach for Face Pose Estimation

Publié en ligne: 27 juin 2024

Pages: 105 - 121

Reçu: 30 mai 2023

Accepté: 21 avr. 2024

DOI: https://doi.org/10.2478/cait-2024-0018

Mots clésFace alignment, Biometrics, Face recognition, Image processing, Landmark detection, Deep convolutional neural network

© 2024 Mayank Kumar Rusia et al., published by Sciendo

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

Mots clés
Face alignment, Biometrics, Face recognition, Image processing, Landmark detection, Deep convolutional neural network