Dual CNN and texture-based face-iris multimodal biometric system via decision-level fusion
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Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Multimodal biometric systems integrate multiple biometric traits to enhance recognition accuracy and robustness. This study introduces a novel face-iris multimodal biometric framework combining texture-based and deep learning methods. The system utilizes uniform local binary patterns applied to capture fine-grained texture features. Additionally, a dual convolutional neural network (CNN) model, incorporating AlexNet and an attention mechanism, extracts high-level discriminative features from entire face and iris images. The attention mechanism prioritizes critical regions in feature maps, improving focus on discriminative details while mitigating noise. The key innovation of the system lies in integrating texture-based and CNN-based features, which collectively enable robust feature extraction and classification. Furthermore, the decision-level fusion strategy using the majority voting technique ensures optimal combination of independent decisions from the methods, providing a resilient final classification decision. Experiments conducted on the CASIA-Iris-Distance database demonstrate a recognition performance of 99.53%, significantly outperforming unimodal and state-of-the-art multimodal systems.
Açıklama
Anahtar Kelimeler
Multimodal biometric System, Information fusion, Decision level fusion, Convolutional neural networks, Dual CNN, Uniform local binary patterns
Kaynak
Signal Image and Video Processing
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
19
Sayı
4
