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Biometrics has developed to be one of the most relevant technologies used in Information Technology (IT) security. Uni-modal biometric systems have a variety of problems which decreases the performance and accuracy of these systems. One way to overcome the limitations of the Unimodal biometric systems is through fusion to form a multimodal biometric system. Hence this process is developed based on Multimodal Biometric system based on Face, Finger print, Signature etc. Multimodal biometric system employing Convolutional Neural Networks (CNNs) for effective feature extraction and classification. The system combines facial and signature biometrics, harnessing the unique advantages of each modality to create a more resilient and accurate authentication framework. Multimodal biometric system with CNN integration holds promise for applications in secure access control, financial transactions, and other domains where reliable authentication is crucial. Its adaptability and scalability make it a viable solution for addressing the evolving challenges in biometric security systems.