Deep Learning-Based Iris Recognition System Using Unprocessed Images

The iris image is a powerful and distinctive feature in biometrics that serves as a reliable instrument for human identification. Extracting significant features is crucial for developing iris-based recognition systems. While preprocessing for iris region detection is typically the first step in such systems, it can often fail in real acquisition conditions, leading to decreased performance. This study proposes a new iris-based recognition system that directly exploits original (noisy) images for feature extraction, avoiding the bottleneck of preprocessing. Additionally, a multimodal architecture is proposed for both right and left iris images to strengthen the decision step. Pretrained CNN models extract features classified by SoftMax and support vector machines (SVM). The performance of the proposed system is tested on four public datasets collected under different conditions. The results show that classification accuracy for the original iris dataset without pre-processing is higher than for the normalized database due to the rich CNN features that provide more cognitive information, resulting in greater discrimination power. The CNN multi-modal recognition system achieves the best accuracy compared to most state-of-the-art models, demonstrating the strength of the proposed fusion recognition system.