Beyond Traditional Methods: Feature Fusion for Robust Biometric Identification

In the era of cybersecurity, Biometric recognition systems are used in a variety of applications, including access control and forensic investigation. This paper presents a novel biometric authentication technique in the field of cybersecurity by combining support vector machine (SVM) classification with feature extraction techniques. Our system combines the symbiotic interaction between several biometric cues to capture detailed information in biometric data by integrating Local Phase Quantization (LPQ) and Local Binary Pattern (LBP) features. SVM classifier efficiently finds discriminative patterns in the fused feature space to enable accurate person identification. Tests carried out empirically on benchmark datasets verify the effectiveness of our approach and demonstrate significant improvements in recognition accuracy compared to traditional methods. Our research has encouraging implications for practical cybersecurity applications by highlighting the importance of feature fusion and SVM classification in strengthening biometric authentication systems.