Age Estimation Using Soft Labelling Ordinal Classification Approaches

Research areas:

• Uncategorized

Year:

2024

Type of Publication:

In Proceedings

Keywords:

Age estimation, soft labelling, ordinal classification

Authors:

• Vargas, Víctor Manuel
• Gómez-Orellana, Antonio Manuel
• Guijo-Rubio, David
• Bérchez-Moreno, Francisco
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Volume:

14640

Book title:

Advances in Artificial Intelligence

Pages:

40-49

Organization:

CAEPIA

Month:

Junio

ISSN:

1611-3349

BibTex:

@conference{AgeEstimation_caepia_2024, author = "V{\'i}ctor Manuel Vargas and Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Francisco B{\'e}rchez-Moreno and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", 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.", awards = "1er premio en congreso CAEPIA-TAMIDA", booktitle = "Advances in Artificial Intelligence ", doi = "https://link.springer.com/chapter/10.1007/978-3-031-62799-6_5", issn = "1611-3349", keywords = "Age estimation, soft labelling, ordinal classification", month = "Junio", organization = "CAEPIA", pages = "40--49", title = "{A}ge {E}stimation {U}sing {S}oft {L}abelling {O}rdinal {C}lassification {A}pproaches", url = "doi.org/10.1007/978-3-031-62799-6_5", volume = "14640", year = "2024", }

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.