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Deep learning analysis and age prediction from shoeprints.

Muhammad Hassan¹, Yan Wang¹, Di Wang²

¹College of Computer Science and Technology, Jilin University, Changchun, China.

Forensic Science International

|September 23, 2021

Summary

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This summary is machine-generated.

Researchers developed ShoeNet, a deep learning model, to predict age from shoeprints. The model achieved significant accuracy, revealing age and gender patterns in foot pressure, aiding forensic and medical studies.

Area of Science:

Biometrics
Forensic Science
Machine Learning

Background:

Human gaits, encompassing limb movements and body forces, are influenced by environmental and physical factors.
Shoeprints capture these gait patterns, offering potential for age prediction, a computationally underexplored area.
Existing methods lack systematic computational approaches for age prediction from shoeprints.

Purpose of the Study:

To develop a deep learning model for analyzing age-related patterns in shoeprints.
To predict age using computational analysis of shoeprint features.
To investigate age and gender-specific patterns in shoeprint pressure distributions.

Main Methods:

Collected 100,000 shoeprints from individuals aged 7 to 80.
Developed ShoeNet, a deep learning end-to-end model integrating convolutional neural networks with a skip mechanism.

Keywords:

Age prediction Aging Deep learning Gait-and-standing patterns Pressure distribution Shoeprint

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Analyzed age-related features, particularly in pressure and abrasion regions, from pair-wise shoeprints.

Main Results:

Achieved a prediction accuracy where 40.23% of subjects had age prediction errors within 5 years.
Reached 86.07% accuracy for gender/sex classification.
Identified age-related features primarily in asymmetric differences between left and right shoeprints.
Observed age-related pressure spreading from toe-middle to outer regions and gender-specific heel pressure variations.

Conclusions:

Deep learning models like ShoeNet can effectively predict age from shoeprints.
Shoeprint analysis reveals significant age and gender-related patterns in pressure distribution.
Findings have implications for forensic investigations, medical gait disorder studies, biometrics, and sports science.