The insights from this study can be extrapolated to other biometric modalities beyond iris and face recognition by understanding the importance of saliency granularity and source diversity in model training. Different biometric modalities may have unique characteristics and challenges, but the fundamental principles of incorporating human-perceptual intelligence through saliency-based training remain applicable. Researchers can adapt the concept of salience granularity levels (BOI, AOI, FOI) and explore the optimal level for each modality. By considering the architecture-dependent nature of saliency granularity, they can tailor the training process to suit the specific requirements of different biometric tasks. Additionally, the use of models trained to mimic human saliency can be extended to other modalities, providing a scalable and efficient way to generate saliency information. Overall, the study's findings offer a framework that can be adapted and refined for various biometric recognition systems.