Age Estimation Using Soft Labelling Ordinal Classification Approaches
Víctor Manuel Vargas-Yun 
Antonio Manuel Gómez-Orellana 
David Guijo-Rubio 
Francisco Bérchez-Moreno 
Pedro Antonio Gutiérrez 
César Hervás-Martínez Advances in artificial intelligence
Abstract
This work explores the use of diverse soft labelling approaches recently proposed in the literature to address four distinct problems in age estimation. This kind of challenge can be considered an ordinal classification problem in machine learning or deep learning areas, as it exhibits a natural order among categories, reflecting the underlying age ranges defining each category. Soft labelling represents a machine learning approach in which, instead of assigning a single label to each instance in the dataset, a probability distribution across a range of labels is allocated. Soft labelling approaches prove particularly effective for age estimation due to the inherent uncertainty and continuity in age progression, which makes accurate age estimation from physical appearance difficult. Unlike categorical labels, age is a continuous variable that evolves over time. Thus, unlike hard labelling, soft labelling more effectively acknowledges the continuity and uncertainty inherent in age estimation. The experiments conducted in this study facilitate the comparison of soft labelling approaches against the nominal baseline. Results demonstrate superior performance of soft labelling approaches. Moreover, the statistical analysis reveals that use of a beta distribution to define soft labels yields the best results.
Keywords
BibTex Citation
@inproceedings{Vargas2024Age,
author = {Vargas-Yun, V{\' i}ctor Manuel and G{\' o}mez-Orellana, Antonio Manuel and Guijo-Rubio, David and B{\' e}rchez-Moreno, Francisco and Guti{\' e}rrez, Pedro Antonio and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar},
booktitle = {Advances in artificial intelligence},
doi = {10.1007/978-3-031-62799-6_5},
year = {2024},
pages = {40--49},
title = {Age {Estimation} {Using} {Soft} {Labelling} {Ordinal} {Classification} {Approaches}},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6_5},
howpublished = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6\textunderscore{}5},
volume = {14640},
}
BibTex Unicode Citation
@inproceedings{Vargas2024Age,
author = {Vargas-Yun, Víctor Manuel and Gómez-Orellana, Antonio Manuel and Guijo-Rubio, David and Bérchez-Moreno, Francisco and Gutiérrez, Pedro Antonio and Hervás-Martínez, César},
booktitle = {Advances in artificial intelligence},
doi = {10.1007/978-3-031-62799-6_5},
year = {2024},
pages = {40--49},
title = {Age {Estimation} {Using} {Soft} {Labelling} {Ordinal} {Classification} {Approaches}},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6_5},
howpublished = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6\textunderscore{}5},
volume = {14640},
}
APA Citation
Vargas-Yun, V. M., Gómez-Orellana, A. M., Guijo-Rubio, D., Bérchez-Moreno, F., Gutiérrez, P. A., & Hervás-Martínez, C. (2024). Age Estimation Using Soft Labelling Ordinal Classification Approaches. Advances in Artificial Intelligence, 14640, 40–49. https://doi.org/10.1007/978-3-031-62799-6_5
RIS Citation
TY - CONF
AU - Vargas-Yun, Víctor Manuel
AU - Gómez-Orellana, Antonio Manuel
AU - Guijo-Rubio, David
AU - Bérchez-Moreno, Francisco
AU - Gutiérrez, Pedro Antonio
AU - Hervás-Martínez, César
C3 - Advances in artificial intelligence
DA - 2024///
C2 - 2024
DO - 10.1007/978-3-031-62799-6_5
ID - temp_id_821915871694
SP - 40-49
TI - Age Estimation Using Soft Labelling Ordinal Classification Approaches
UR - https://link.springer.com/chapter/10.1007/978-3-031-62799-6_5
VL - 14640
ER -
CV Citation
V.M. Vargas-Yun, A.M. Gómez-Orellana (CA), D. Guijo-Rubio, F. Bérchez-Moreno, P.A. Gutiérrez, C. Hervás-Martínez (1/6). "Age Estimation Using Soft Labelling Ordinal Classification Approaches". Advances in artificial intelligence, pp. 40–49, 2024.