Advancing multimodal biometric image retrieval with sparse spectral graph convolution network and banyan tree growth optimization

Recently, multimodal biometric systems have gained attention for enhancing recognition accuracy and robustness, yet they still face issues like noise interference, redundant features, low accuracy, and inefficient data integration. To overcome these complications, Advancing Multimodal Biometric Image Retrieval with Sparse Spectral Graph Convolution network and Banyan Tree Growth Optimization (MBI-SSGCN-BTGO) is proposed. Here, the input images are collected from soco-fingerprint-female-and-male, face-recognition, and mmu-iris-datasets. The input images are preprocessed using the Fast Guided Median Filter (FGMF) for contrast correction, image scaling, cropping, and normalization. Afterward, the Holistic Dynamic Frequency Transform (HDFT) is used to extract features from images. Then, Snow Ablation Optimization (SAO) is used to choose the most relevant features. The optimal features are used for image retrieval, aiding in identity verification prior to classification. The classification is done by Sparse Spectra Graph Convolutional Network (SSGCN) to classify the biometric system, such as woman and man for face-recognition dataset, female and male for soco-fingerprint-female-and-male dataset and left and right for mmu-iris-dataset. Finally, the Banyan Tree Growth Optimization (BTGO) algorithm is employed to optimize the weight parameters of SSGCN. By integrating BTGO, the model efficiently identifies optimal feature representations, improving convergence speed and overall classification performance. The proposed MBI-SSGCN-BTGO approach is implemented in Python and its performance is examined undersome metrics. The performance of MBI-SSGCN-BTGO technique attains 16.17 %, 17.43 %, 19.23 % lower False Acceptance Rate (FAR) and 29.45 %, 28.42 % and 29.11 % higher precision and 26.17 %, 27.43 % when compared with existing techniques respectively.