A Feature Fusion-Based ResNet Using the Pooling Pyramid for Age Estimation

In controlled settings, facial age estimation models have achieved remarkable accuracy. However, in the uncontrolled or "in the wild" scenarios, the performance of these models faces significant challenges. Factors such as varying image resolutions, environmental lighting conditions, and obstructions substantially impact the effectiveness of age estimation in these less constrained environments. This complexity highlights the ongoing need for advancements in the field to address the unique challenges presented by the real-world conditions. To solve this problem, we propose a novel model that combines Convolutional Neural Networks (CNN) and Vision Transformer architectures. We have made a strategic modification to the ResNet architecture, which forms the backbone of our model. A significant enhancement in our model is the replacement of traditional convolutions with depth-wise separable convolutions, which offers improved efficiency and reduced model complexity. In addition, we replace the last three bottleneck layers with the components of the Vision Transformer. This alteration allows for a more sophisticated and nuanced feature extraction process, particularly beneficial for complex image scenarios encountered in the uncontrolled settings. Additionally, our model incorporates a feature pooling pyramid structure to facilitate the multi-scale feature fusion. This innovative combination not only leverages the strengths of both CNN and Transformers, but also enhances the feature extraction and representation capabilities across various scales, making it particularly effective for diverse vision-based tasks. We evaluate our model on three uncontrolled popular datasets and validate the effectiveness of the proposed method.