The rapid advancements in deep learning and classifier fusion techniques offer promising solutions to enhance the accuracy and robustness of biometric authentication systems in this paper we propose the integration of these methodologies, specifically in multimodal biometric systems that utilize face and fingerprint recognition. The research investigates various deep learning architectures, highlighting their effectiveness in processing diverse biometric datasets. Additionally, it examines classifier fusion techniques, which combine multiple classifiers to improve person identification performance. A significant focus of this research is on spoofing and anti-spoofing measures. Biometric systems, especially those involving facial and fingerprint recognition, are vulnerable to spoofing attacks such as the use of photographs, videos, or artificial fingerprints to impersonate legitimate users. We developed various anti-spoofing strategies that are integrated into the biometric authentication process to mitigate these risks. These include techniques like texture analysis, motion analysis, and liveness detection, which help differentiate between genuine biometric traits and spoofed samples. We benchmarked a comparative analysis of deep learning models and classifier fusion, demonstrated their strengths, weaknesses, and best practices. Additionally, performance evaluations focus on key metrics such as accuracy, computational efficiency, scalability, and the system’s ability to resist spoofing attacks. Ultimately, the paper emphasizes the potential of these advanced techniques to revolutionize biometric systems, with a particular focus on future research directions for optimizing these methodologies, particularly in the context of improving robustness against spoofing and enhancing the overall security of biometric authentication systems. overall system Equal Error Rate (EER), the True Acceptance Rate at a specified False Acceptance Rate (e.g., TAR @ 0.1% FAR), and the accuracy of the anti-spoofing module.