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An Efficient Hyperdimensional Computing Paradigm for Face Recognition
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AI
DOI：10.1109/ACCESS.2022.3197668.png)
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Abstract
En 中文
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.
Keywords:
Face recognition
Feature extraction
Training data
Principal component analysis
Support vector machines
Biological neural networks
Classification algorithms
Computer vision
Learning systems
Neural networks
Computer vision
facial recognition
HD computing
incremental learning
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W