Face Recognition Implementation Combining LPP and LDA Algorithms

We have implemented a face recognition method based on the integration of LPP and LDA algorithms. This hybrid approach effectively combines the strengths of both techniques to achieve superior face recognition performance. Specifically, the LPP algorithm preserves the local neighborhood structure in the feature space, which helps extract robust facial features by maintaining the intrinsic geometric relationships between similar face images. In code implementation, this typically involves constructing an adjacency graph and solving a generalized eigenvalue problem to obtain projection vectors that minimize local scatter.

The LDA algorithm further enhances recognition accuracy by maximizing between-class variance while minimizing within-class variance, effectively improving discriminative power across different facial identities. Programmatically, LDA computes optimal projection directions by solving the eigenproblem for Sb (between-class scatter matrix) and Sw (within-class scatter matrix). By sequentially applying LPP for local structure preservation and LDA for class separation, our algorithm achieves significantly improved robustness and accuracy in face recognition tasks. The implementation typically involves preprocessing face images, computing feature projections using both algorithms, and employing a classifier (e.g., k-NN or SVM) for final identification.