Authors - Rabab A. Rasool, Muthana H. Hamd Abstract - This paper introduces a novel unimodal biometric system based on a derivative angle-based feature. Unlike traditional edge-based features, this approach leverages a one-to-one correlation between the angle and its corresponding edge, enabling robust and reliable recognition for a single-source trait biometric system. This unique property allows for performance comparable to multimodal systems, which typically rely on multi-source traits fusion techniques for improved accuracy. To evaluate the effectiveness of angle-based features, an extensive experiments is conducted on three diverse facial datasets (AR, FEI, and CK+) using 150 subjects. The facial features are extracted using seven distinct methods at varying levels, ensuring a comprehensive and fair comparison between edge and angle-based approaches. Recognition accuracy was assessed using various metrics, including False Acceptance Rate (FAR), False Rejection Rate (FRR), and three Error Distance Measures (EDMs): Euclidean, Manhattan, and Cosine distances. Results demonstrate that angle-based features achieve highly competitive performance compared to their edge-based counterparts. Across 210 recognition processes, edge-based features achieved an overall accuracy of 79%, while angle-based features recorded a closely comparable 73%. These findings highlight the potential of angle-based features as a promising approach for developing robust and reliable unimodal biometric systems.
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