Ear Recognition Using Fisher Method with PCA Dimensionality Reduction and LDA Feature Extraction

This research focuses on implementing ear recognition using the Fisher method by combining PCA (Principal Component Analysis) dimensionality reduction and LDA (Linear Discriminant Analysis) feature extraction. PCA dimensionality reduction aims to reduce data dimensions while preserving critical variance, typically implemented through eigenvalue decomposition of the covariance matrix to transform high-dimensional ear images into lower-dimensional feature vectors. LDA feature extraction enhances inter-class discrimination by maximizing between-class variance and minimizing within-class variance, achieved by calculating scatter matrices and solving generalized eigenvalue problems. The integration of both methods involves first applying PCA to reduce computational complexity and eliminate noise, followed by LDA to extract highly discriminative features. This hybrid approach enables more accurate ear identification by maintaining essential data characteristics while improving class separability, thereby providing more reliable technical support for applications in biometric security and pattern recognition systems.