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Face membership authentication using SVM classification tree generated by membership-based LLE data partition.

Shaoning Pang¹, Daijin Kim, Sung Yang Bang

¹Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand. spang@aut.ac.nz

IEEE Transactions on Neural Networks

|March 25, 2005

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This study introduces a novel face authentication method using a support vector machine (SVM) classification tree. This approach enhances stability and accuracy by dynamically partitioning data for improved membership authentication.

Area of Science:

Computer Science
Biometrics
Machine Learning

Background:

Traditional membership authentication methods face challenges with dynamic group sizes and member changes.
Previous support vector machine (SVM) ensemble methods offered robustness but lacked stability under varying group sizes.

Purpose of the Study:

To develop a novel membership authentication method using a support vector machine (SVM) classification tree.
To improve the stability and dynamic adaptability of face classification systems for membership authentication.

Main Methods:

A divide and conquer strategy was employed, utilizing recursive data partitioning via membership-based Locally Linear Embedding (LLE) clustering.
Support vector machine (SVM) classification was then performed on each partitioned feature subset.

Related Experiment Videos

Main Results:

The proposed SVM tree maintained the high authentication accuracy and robustness to member changes characteristic of SVM ensemble methods.
A considerable improvement in stability was observed when the membership group size changed dynamically.

Conclusions:

The SVM classification tree offers a superior approach to membership authentication compared to previous SVM ensemble methods.
This method provides enhanced stability and dynamic scalability for face-based membership authentication systems.