Principal Component Analysis-Linear Discriminant Analysis Feature Extractor for Pattern Recognition
Year: 2012
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Khan, Aamir, Farooq, Hasan
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2247334854