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Face Detection Using Eigenfaces: A Comprehensive Review
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DOI：10.1109/ACCESS.2024.3435964.png)
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摘要
En 中文
This paper thoroughly reviews face detection techniques, primarily focusing on applying Eigenfaces, a powerful method rooted in Principal Component Analysis (PCA). The goal is to provide a comprehensive understanding of the advancements, challenges, and prospects associated with Eigenface-based face detection systems. The review commences with exploring the comprehensive facial recognition system framework using Eigenfaces and studying the intricacies of employing Eigenfaces as a foundational element for robust facial recognition. Then, we describe the taxonomies of various Eigenface-based face detection approaches to provide a systematic understanding of the diverse strategies utilized in Eigenface-based face detection systems. Besides, the paper explores benchmarking datasets tailored specifically for facial recognition. These datasets are critically analyzed, highlighting their relevance, limitations, and potential impact on developing and assessing Eigenface-based face detection algorithms. Furthermore, the review details the limitations and open issues inherent in Eigenface-based face detection systems. Addressing concerns such as sensitivity to lighting conditions, occlusions, and scalability, this section aims to guide future research directions by identifying gaps in the current understanding and proposing potential avenues for improvement.
Keyword:
Face recognition
Vectors
Face detection
Eigenvalues and eigenfunctions
Reviews
Mathematical models
Covariance matrices
Dimensionality reduction
eigenvalues and eigenfunctions
face detection
face recognition
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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