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An Efficient Hyperdimensional Computing Paradigm for Face Recognition
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DOI：10.1109/ACCESS.2022.3197668.png)
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摘要
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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.
Keyword:
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
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors超维计算: 高维随机向量分布式表示计算简介
PREAI
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OAAI分享收藏Fusion facial semantic feature and incremental learning mechanism for efficient face recognition融合人脸语义特征和增量学习机制实现高效人脸识别SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
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