Wavelet Transform-Based Face Recognition Code with Image Decomposition
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Wavelet transform-based face recognition code employs wavelet decomposition to process facial images, extracting detailed features through multi-resolution analysis. The implementation typically involves applying discrete wavelet transform (DWT) to decompose input images into approximation and detail coefficients, capturing both global features and local textures. The nearest neighbor classifier operates by comparing feature vectors of test images against trained templates using distance metrics like Euclidean or Manhattan distance. Key functions include wavelet decomposition routines for feature extraction and similarity measurement algorithms for classification. By integrating wavelet-based feature extraction with nearest neighbor matching, this approach achieves improved recognition accuracy in facial identification systems while maintaining computational efficiency through dimensionality reduction in the wavelet domain.
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