Age detection using facial images has been an active area of research in recent years. Deep
learning approaches, in particular, have shown great potential in achieving high accuracy and
efficiency in this task. In this study, we present a comprehensive investigation of the use of
VGGFace, a deep neural network pre-trained on a large dataset of faces, for age detection. We
first explore the impact of pre-processing techniques, such as normalization and augmentation,
on the performance of the VGGFace network. We then compare the performance of different
variants of the VGGFace architecture for age detection. We evaluate the performance of the
network on several benchmark datasets, including the IMDB-WIKI dataset, and report the
accuracy and efficiency of the approach. Our results show that pre-processing techniques such
as normalization and augmentation can significantly improve the accuracy of the VGGFace
network for age detection. We also find that some variants of the VGGFace architecture, such
as VGG16 and VGG19, perform better than others. Overall, this study provides a
comprehensive investigation of the use of VGGFace for age detection, and sheds light on the
impact of pre-processing techniques and model selection on the performance of the approach.
Our findings can help researchers and practitioners to develop more accurate and efficient age
detection systems using deep learning