Biometrics, as an identification method, is used for various applications, particularly in security technologies. The integration of multiple biometric sources aims to overcome limitations observed in unimodal systems, enhancing recognition accuracy. Fusion techniques, categorized into sensor level, feature level, matching score level, decision level, and rank level, are explored to optimize the combination of information from different modalities. Various fusion schemes, such as feature-level fusion, decision-level fusion, and hybrid systems, are investigated for their effectiveness in integrating diverse biometric traits. This paper details the fusion schemes at different levels, including sensor, feature extraction, matching score, and decision levels. Experimental results demonstrate the efficacy of the proposed multimodal biometric system. The Equal Error Rate (EER) is analyzed to evaluate system accuracy, with weights assigned to each modality based on their performance. Normalization techniques and fusion rules are applied to combine modalities, resulting in enhanced matching scores. The analysis of results showcases the performance of the system across various fusion combinations. Notably, the combination of ear and foot modalities yields the highest matching score, demonstrating the effectiveness of fusion techniques in multimodal biometrics.