Mathematical Modelling for Neural Network-Based Textural Feature Extraction from Normalized Ocular Images for Improved Biometric Authentication

Biometric authentication systems have become increasingly prevalent in security and identity verification applications. Among various biometric traits, the ocular region, specifically the iris, offers a unique and stable source for individual identification. The intricate patterns within the iris are highly distinctive and remain consistent over a person's lifetime, making them ideal for biometric applications. In the field of biometric authentication, the ocular region has emerged as a robust and reliable source for identity verification due to its unique and stable features. This study focuses on enhancing the accuracy of biometric systems through neural network-based textural feature extraction from normalized ocular images. The proposed approach utilizes Mask Image Extraction (Hough circle) and an efficient U-Net model to achieve superior performance in biometric authentication. Initially, the ocular images are pre-processed to normalize lighting and orientation, ensuring consistency across the dataset. Mask Image Extraction, particularly using the Hough circle transform, is employed to isolate the region of interest (ROI) within the ocular images. This method accurately detects and segments the circular iris region, which is crucial for subsequent feature extraction. The core of the proposed system is the Efficient U-Net model, a variant of the standard U-Net architecture designed for enhanced efficiency and accuracy. The U-Net model is trained to extract intricate textural features from the segmented ocular regions. By leveraging its encoder-decoder structure, the U-Net captures both local and global features, resulting in a detailed and discriminative representation of the ocular texture. Experimental results demonstrate that the integration of Mask Image Extraction and the Efficient U-Net model significantly improves the accuracy of biometric authentication systems. The proposed method outperforms traditional approaches by providing more precise and reliable feature extraction, thereby enhancing the overall robustness and reliability of ocular-based biometric systems.