Principal Component Analysis-Linear Discriminant Analysis Feature Extractor for Pattern Recognition
This work addresses robustness in embedded biometric systems for security applications, but it appears incremental as it combines existing methods.
The paper tackles the problem of high-dimensional patterns in biometric identity verification by proposing a multimodal system using PCA and LDA with KNN, achieving good pattern recognition results as implemented in real-time with SignalWAVE.
Robustness of embedded biometric systems is of prime importance with the emergence of fourth generation communication devices and advancement in security systems This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional image space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Principal Component Analysis and Lindear Discriminant Analysis with K-Nearest Neighbor and implementing such system in real-time using SignalWAVE.