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

<p>Face orientation recognition is a crucial component of human behavior analysis. Challenges such as face occlusion and varying lighting conditions frequently degrade recognition accuracy. This study introduces an innovative approach using the Kinect sensor’s skeleton tracking technology to capture face orientation images across different lighting scenarios. To enhance image quality, techniques such as light attenuation and compensation are applied. Furthermore, multiple feature vectors based on face orientation are extracted and fused, followed by the optimization of the Extreme Learning Machine (ELM) model through the Archimedean Optimization Algorithm (AOA). The developed AOA-ELM model for face orientation recognition is compared with traditional models, including Back Propagation (BP), Radial Basis Function (RBF), standard ELM, and Genetic Algorithm enhanced ELM (GA-ELM). The AOA-ELM model exhibits superior performance in estimating face orientation amidst lighting disturbances, achieving an accuracy exceeding 95% in varied lighting conditions, thereby outperforming other classification models.</p>
</abstract>

Face orientation recognition is a crucial component of human behavior analysis. Challenges such as face occlusion and varying lighting conditions frequently degrade recognition accuracy. This study introduces an innovative approach using the Kinect sensor’s skeleton tracking technology to capture face orientation images across different lighting scenarios. To enhance image quality, techniques such as light attenuation and compensation are applied. Furthermore, multiple feature vectors based on face orientation are extracted and fused, followed by the optimization of the Extreme Learning Machine (ELM) model through the Archimedean Optimization Algorithm (AOA). The developed AOA-ELM model for face orientation recognition is compared with traditional models, including Back Propagation (BP), Radial Basis Function (RBF), standard ELM, and Genetic Algorithm enhanced ELM (GA-ELM). The AOA-ELM model exhibits superior performance in estimating face orientation amidst lighting disturbances, achieving an accuracy exceeding 95% in varied lighting conditions, thereby outperforming other classification models.

Face orientation recognition based on Kinectv2 and an improved ELM model

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Face orientation recognition based on Kinectv2 and an improved ELM model"}

Face orientation recognition is a crucial component of human behavior analysis. Challenges such as face occlusion and varying lighting conditions frequently...

Face orientation recognition based on Kinectv2 and an improved ELM model

Pubblicato online: 07 giu 2024

Pagine: -

Ricevuto: 10 mar 2024

Accettato: 13 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1419

Parole chiave
Face orientation recognition, ELM, AOA, Facial orientation features, Kinect

© 2024 Houcheng Li et al., published by Sciendo

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

Face orientation recognition based on Kinectv2 and an improved ELM model

Pubblicato online: 07 giu 2024

Pagine: -

Ricevuto: 10 mar 2024

Accettato: 13 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1419

Parole chiaveFace orientation recognition, ELM, AOA, Facial orientation features, Kinect

© 2024 Houcheng Li et al., published by Sciendo

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

Parole chiave
Face orientation recognition, ELM, AOA, Facial orientation features, Kinect