Improving Face-Based Age Estimation with Attention-Based Dynamic Patch Fusion

TL;DR

This paper introduces an attention-based dynamic patch fusion framework for face-based age estimation, which dynamically identifies and ranks age-specific facial patches to improve prediction accuracy.

Contribution

The novel RMHHA mechanism and diversity loss enable the model to focus on important, diverse facial patches, enhancing age estimation performance over existing methods.

Findings

Outperforms state-of-the-art on benchmark datasets

Effectively ranks patches by importance

Reduces overlap among patches for diversity

Abstract

With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information. In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF). In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet. The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism. The FusionNet uses the discovered patches along with the facial image to predict the age of the subject. Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important). ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered. Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.