Face Recognition Implementation Using PCA+KNN Algorithm with 2DPCA Approach

In this article, we present a face recognition approach based on PCA+KNN algorithm. This implementation utilizes a 2DPCA-based method that offers significant advantages in computational efficiency, particularly through reduced processing time. We will provide detailed explanations of the algorithm implementation, including how 2DPCA extracts features directly from image matrices without vectorization, followed by KNN classification for pattern matching. The implementation typically involves key functions such as covariance matrix calculation for 2DPCA, eigenvalue decomposition for feature projection, and distance measurement in KNN classification. Additionally, we will discuss the advantages and limitations of this methodology, along with potential improvements for enhanced recognition accuracy. Overall, we believe this article will provide valuable insights and practical information for readers interested in facial recognition algorithms and their code implementations.