LDA: A Fundamental Algorithm in Face Recognition Systems
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LDA (Linear Discriminant Analysis) represents a fundamental algorithm in face recognition systems. It performs feature extraction and dimensionality reduction on facial images to effectively identify and distinguish between different faces. LDA finds extensive application in facial recognition domains due to its significant practical value. Through LDA implementation, we can achieve more accurate and reliable facial identification by maximizing inter-class variance while minimizing intra-class variance using scatter matrix calculations. The algorithm typically involves computing within-class and between-class scatter matrices, followed by eigenvalue decomposition to obtain optimal projection vectors. This enhances both the performance and application scope of face recognition technology, with key functions including data preprocessing, covariance matrix computation, and discriminant feature transformation.
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