Implementation of the Linear Discriminant Analysis Algorithm for Feature Extraction in Face Pattern Recognition

In recent years, biometric technologies such as retina scanning, fingerprint recognition, voice recognition, and facial recognition have advanced rapidly and been widely implemented, particularly in security systems for identification and verification. Among these modalities, the human face possesses unique characteristics that make it highly suitable for recognition systems. However, facial recognition by computers remains a complex task that requires highly accurate algorithms. Deep learning has played a crucial role in improving the accuracy and speed of facial recognition systems, particularly in handling lighting variations and pose changes. This study focuses on the implementation of the Linear Discriminant Analysis (LDA) algorithm for feature extraction and facial classification. LDA has been proven to reduce data dimensionality without losing essential information and improve accuracy by maximizing inter-class differences while minimizing intra-class variations. Therefore, LDA offers an effective solution for developing structured, efficient, and accurate facial recognition systems.