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Hierarchical Attention-Based Age Estimation and Bias Analysis.

Shakediel Hiba, Yosi Keller

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 26, 2023

Summary

This study introduces a novel Deep Learning method for accurate facial age estimation. The approach uses advanced image augmentation and a hierarchical probabilistic model, achieving state-of-the-art results on benchmark datasets.

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Accurate age estimation from facial images is crucial for various applications.
Existing methods face challenges in handling image variations and achieving high precision.

Purpose of the Study:

To develop a novel Deep Learning approach for precise age estimation from facial images.
To introduce an attention-based image augmentation-aggregation technique and a hierarchical probabilistic regression model.

Main Methods:

Utilized a Transformer-Encoder for adaptive aggregation of multiple image augmentations.
Developed a hierarchical probabilistic regression model combining discrete estimates and an ensemble of regressors.
Trained regressors to refine probability estimates within specific age ranges.

Main Results:

The proposed age estimation scheme significantly outperforms current state-of-the-art methods.
Achieved new state-of-the-art accuracy on the MORPH II and CACD datasets.
Presented an analysis of biases present in current age estimation techniques.

Conclusions:

The novel Deep Learning approach offers superior accuracy for facial age estimation.
The attention-based augmentation and hierarchical regression model are effective for this task.
The findings contribute to advancing the field of automated facial age analysis.