Discrete Cosine Transform-Based Kernel Discriminant Analysis for Enhanced Biometric Recognition

This study presents a comprehensive exploration of the Discrete Cosine Transform (DCT) and its application in biometric recognition systems, with a specific focus on improving the discriminative capabilities of existing methods.The inherent properties of the DCT are leveraged, and the traditional Linear Discriminant Analysis (LDA) framework is extended to nonlinear scenarios through the design of a novel DCT-based Kernel Discriminant Analysis (DCT-KDA) algorithm.The proposed approach integrates the advantages of DCT with the flexibility of kernel methods to achieve enhanced feature extraction and classification performance.Rigorous experiments are conducted using the FERET and AR face databases to evaluate the effectiveness of the algorithm under various conditions.Results demonstrate that the DCT-KDA method consistently outperforms conventional techniques such as standard KDA and DCT-LDA, delivering superior recognition accuracy and faster computational performance.These improvements highlight the potential of the proposed DCT-KDA framework for real-time biometric applications.By providing a robust, efficient, and scalable solution, this research contributes to advancing the field of biometric recognition, offering insights into both theoretical developments and practical implementations.