Abstract: Reliable person identification is critical for modern security systems, yet unimodal biometric methods often suffer from noise, environmental variations, and user-related differences. To overcome these limitations, this paper presents a robust deep learning–based multimodal biometric system for person identification using deep feature fusion of iris and retina traits. Separate convolutional neural networks are used to extract highly discriminative features from iris and retinal images, capturing complementary information from each modality. The extracted....
Key Word: Multimodal Biometrics, Iris Recognition, Retina Recognition, Deep Learning, Feature Fusion
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