Abstract:
Transformers have recently gained signifcant attention in machine learning due
to their self-attention mechanisms, which allow models to dynamically assess
the importance of different input elements. Although originally designed for
Natural Language Processing (NLP), the application of transformers in computer
vision tasks, such as image classifcation, has been gaining traction. This work
explores the use of Vision Transformers (ViT) in the context of face age
regression, focusing on three well-known datasets: MORPH II, AFAD, and CACD.
By leveraging ViT in a regression setting, we aim to predict the age of individuals
based on facial images. We evaluate the model’s performance using the Mean
Absolute Error (MAE) on each of these datasets and compare it to traditional
models like Convolutional Neural Networks (CNNs). Furthermore, we investigate
the computational efciency and performance gains from transfer learning using
pre-trained ViT models on the ImageNet dataset. Our experiments demonstrate
that Vision Transformers offer a competitive alternative to CNNs for face age
regression, with promising results across all three datasets, showing their
potential for future applications in age estimation and facial analysis.
