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Updated: Dec 13, 2025
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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
Published on: December 24, 2015
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Feature fusion via Deep Random Forest for facial age estimation
O Guehairia1, A Ouamane2, F Dornaika3
1Laboratory of LESIA, University of Biskra, Biskra, Algeria.
Summary
This study introduces a novel Deep Random Forest (DRF) architecture for accurate human age estimation from facial images. The proposed method enhances feature representation and fuses information for improved age prediction, outperforming existing techniques.
Area of Science:
- Computer Vision
- Machine Learning
- Biometrics
Background:
- Human age estimation from facial images is a growing research area with diverse applications.
- Existing methods face challenges in accurately predicting age due to inherent variations in facial features and aging patterns.
Purpose of the Study:
- To propose a novel architecture for human age estimation using facial images.
- To enhance feature representation and incorporate age fuzziness for more robust age prediction.
Main Methods:
- The proposed architecture utilizes a cascade of Deep Random Forests (DRF).
- Two types of DRF are employed: one for feature enhancement and another for fused representation and prediction.
- Off-the-shelf deep features are integrated with the DRF architecture.
Main Results:
- The proposed architecture demonstrates superior performance compared to state-of-the-art methods.
- Experiments conducted on six public databases validate the effectiveness of the approach.
Conclusions:
- The novel DRF-based architecture offers an effective solution for human age estimation from facial images.
- The method successfully integrates deep features with enhanced representation and age fuzziness for improved accuracy.
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