An Efficient Hyperdimensional Computing Paradigm for Face Recognition

In this paper, a combined framework is proposed that includes Hyperdimensional (HD) computing, neural networks, and k-means clustering to fulfill a computationally simple incremental learning framework in a facial recognition system. The main advantages of HD computing algorithms are the simple comp...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.85170-85179
Main Authors: Yasser, Mohammad, Hussain, Khaled F., Ali, Samia Abd El-Fattah
Format: Article
Language:English
Subjects:
Algorithms
Biological neural networks
Classification algorithms
Classifiers
Cluster analysis
Clustering
Computation
Computer vision
Datasets
Face recognition
facial recognition
Feature extraction
HD computing
High resistance
incremental learning
Learning
Learning systems
Neural networks
Principal component analysis
Support vector machines
Training data
Vector quantization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a combined framework is proposed that includes Hyperdimensional (HD) computing, neural networks, and k-means clustering to fulfill a computationally simple incremental learning framework in a facial recognition system. The main advantages of HD computing algorithms are the simple computations needed, the high resistance to noise,and the ability to store excessive amounts of information into a single HD vector. The problem of incremental learning revolves around the ability to regularly update the knowledge within the framework to include new subjects in an online manner. Using an HD computing classifier proved efficient and highly accurate to implement an incremental learning framework as no re-training was required after each online update to the framework wbich is HD computing biggest advantage. Another advantage is that HD computing classifiers can achieve a high degree of generalization. The framework was tested on a total of 11 open source benchmark data sets. A number of experimental tests were preformed to ensure consistent performance of the framework under different conditions against different data sets.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3197668