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Persistent homology classification algorithm.

PeerJ. Computer science·2023

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Filipino sign language alphabet recognition using Persistent Homology Classification algorithm.

Cristian B Jetomo¹, Mark Lexter D De Lara¹

¹Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines Los Baños, Los Baños, Laguna, Philippines.

Peerj. Computer Science

|March 10, 2025

Summary

Topological Data Analysis (TDA) offers a promising alternative for Filipino Sign Language (FSL) recognition. A TDA-inspired classifier, PHCA, demonstrated comparable performance to Support Vector Machines (SVM), showing robustness and stability.

Keywords:

Classification algorithm Filipino sign language Persistent homology Sign language recognition Topological data analysis

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Area of Science:

Computer Science
Artificial Intelligence
Computational Topology

Background:

Communication challenges persist for the growing deaf population, with limited support for Filipino Sign Language (FSL).
Deep networks for FSL recognition face limitations including low transparency, interpretability issues, and high data/computational demands.

Purpose of the Study:

To explore Topological Data Analysis (TDA) as a novel approach for Filipino Sign Language (FSL) recognition.
To evaluate the performance of a TDA-inspired classifier, Persistent Homology Classification Algorithm (PHCA), against traditional classifiers for static FSL alphabet recognition.

Main Methods:

Implemented and evaluated the Persistent Homology Classification Algorithm (PHCA) for classifying static FSL signs.
Compared PHCA's performance against classical classifiers, including Support Vector Machine (SVM), using balanced and imbalanced datasets with hyperparameter tuning.

Main Results:

PHCA achieved a mean accuracy of 99.45%, performing comparably to Support Vector Machine (SVM) at 99.31%.
PHCA demonstrated robustness and stability against data perturbations and noise.
PHCA exhibited challenges in classifying FSL signs with similar gestures, a common issue in FSL recognition.

Conclusions:

PHCA presents a viable alternative for Filipino Sign Language (FSL) recognition, offering advantages in transparency and interpretability over deep networks.
The study highlights the potential of Topological Data Analysis (TDA) in addressing limitations of current machine learning models for sign language recognition.