Face Recognition Using Linear Discriminant Analysis (LDA)

This article discusses face recognition using Linear Discriminant Analysis (LDA). LDA, a classical machine learning algorithm, is commonly employed for classification and dimensionality reduction tasks. In face recognition applications, LDA projects facial images into a lower-dimensional space to extract the most discriminative features, thereby improving recognition accuracy. The algorithm works by maximizing between-class variance while minimizing within-class variance, typically implemented through eigenvalue decomposition of scatter matrices. In practical implementation, the LDA face recognition process involves several key steps: preprocessing facial images, computing within-class and between-class scatter matrices, solving the generalized eigenvalue problem to obtain optimal projection vectors, and projecting new facial images onto the LDA subspace for classification using distance metrics like Euclidean or Mahalanobis distance. The technology finds applications in various domains including facial recognition access control systems and biometric payment systems. However, it's important to note that LDA-based face recognition has certain limitations - it demonstrates reduced robustness to variations in facial pose, lighting conditions, and expressions. These challenges often require additional preprocessing techniques or algorithm optimizations in real-world deployments, such as incorporating histogram equalization for illumination normalization or using ensemble methods with other classifiers.