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Related Experiment Video

Updated: Jun 11, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Depth-induced bipolar neural collapse for privacy-preserving face verification.

Yen-Lung Lai¹, Wun-She Yap¹, Bok-Min Goi¹

¹Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, 48000, Malaysia.

Neural Networks : the Official Journal of the International Neural Network Society

|June 9, 2026

Summary

This study introduces Bipolar Neural Collapse (BNC), a training-free phenomenon emerging from fixed-weight networks, enabling privacy-preserving face verification through cryptographic hashing and controlled depth.

Keywords:

Contrastive learning Fixed-weight feature transformation Neural collapse Privacy-preserving face verification Unsupervised learning

Related Experiment Videos

Last Updated: Jun 11, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Neural Collapse (NC) describes feature convergence in deep networks during supervised training.
Existing research links NC to label-driven gradient optimization.
A distinct, training-free regime called Bipolar Neural Collapse (BNC) is unexplored.

Purpose of the Study:

To investigate a label-free, training-free mechanism for generating stable representations.
To explore the potential of BNC for privacy-preserving face verification.
To analyze the impact of transformation depth on BNC geometry and utility.

Main Methods:

A fixed-weight multilayer transformation applied to pretrained facial embeddings.
Utilized top-k selective routing and unit-norm projection without backpropagation.
Evaluated on LFW, CFP, and CMU-PIE benchmarks with varying transformation depths.

Main Results:

The transformation induces a low-rank bias, polarizing embeddings into antipodal clusters.
This BNC geometry enables one-way cryptographic hashing for face verification without raw data storage.
Moderate depth enhances verification utility, while excessive depth risks over-collapse and increased collisions.

Conclusions:

Depth-induced BNC geometry offers a controllable, training-free approach to representation learning.
It balances face verification utility with privacy-preserving protected-template security.
Findings suggest BNC as a novel mechanism for secure and efficient biometric systems.