Feature-Level vs. Score-Level Fusion in the Human Identification System

Abstract

The design of a robust human identification system is in high demand in most modern applications such as internet banking and security, where the multifeature biometric system, also called feature fusion biometric system, is one of the common solutions that increases the system reliability and improves recognition accuracy. This paper implements a comprehensive comparison between two fusion methods, named the feature-level fusion and score-level fusion, to determine which method highly improves the overall system performance. The comparison takes into consideration the image quality for the six combination datasets as well as the type of the applied feature extraction method. The four feature extraction methods, local binary pattern (LBP), gray-level co-occurrence matrix (GLCM), principle component analysis (PCA), and Fourier descriptors (FDs), are applied separately to generate the face-iris machine vector dataset. The experimental results highlighted that the recognition accuracy has been significantly improved when the texture descriptor method, such as LBP, or the statistical method, such as PCA, is utilized with the score-level rather than feature-level fusion for all combination datasets. The maximum recognition accuracy is obtained at 97.53% with LBP and score-level fusion where the Euclidean distance (ED) is considered to measure the maximum accuracy rate at the minimum equal error rate (EER) value.