A comparative study of facial feature classification methods

Martin Gonzalez-Ruiz; Victor H. Díaz-Ramírez; Miguel Cazorla; Rigoberto Juarez-Salazar

doi:10.1117/12.3027562

30 September 2024 A comparative study of facial feature classification methods

Martin Gonzalez-Ruiz, Victor H. Díaz-Ramírez, Miguel Cazorla, Rigoberto Juarez-Salazar

Author Affiliations +

Proceedings Volume 13136, Optics and Photonics for Information Processing XVIII; 131360K (2024) https://doi.org/10.1117/12.3027562
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

A crucial task in facial expression recognition is the classification of facial features in captured images. This classification task is challenging because facial features change dynamically due to several facial expressions. Additionally, the captured face images are often degraded by additive noise, nonuniform illumination, geometrical modifications, and partial occlusions, increasing uncertainty in classification . Several successful methods for facial landmark classification based on machine learning have been proposed. This work presents a comparative study of existing classification methods for facial landmarks in image sequences degraded by noise, nonuniform illumination, and partial occlusions. The performance of the classification methods considered in the study is quantified in terms of accuracy using face images from well-known datasets. The study aims to provide useful insights into the efficacy of existing facial landmark classification methods under challenging conditions.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Martin Gonzalez-Ruiz, Victor H. Díaz-Ramírez, Miguel Cazorla, and Rigoberto Juarez-Salazar "A comparative study of facial feature classification methods", Proc. SPIE 13136, Optics and Photonics for Information Processing XVIII, 131360K (30 September 2024); https://doi.org/10.1117/12.3027562

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