Application and analysis of face matching based on the Siamese model in face recognition

Abstract

In recent years, face recognition technology, and face matching particularly have broadened the application fields in various aspects of society. It is considered a combination of deep learning architecture and face recognition technology, which has been used for personal information security and safety efficiently for many years. For this, this paper aims to investigate the practical method of utilizing Siamese models to enhance the accuracy and efficiency of face matching systems. The existing challenges of low accuracy and slow recognition rates in face matching applications have been approved to be solvable by utilizing the capabilities of the Siamese model. Experimental analysis and comments from relevant practitioners demonstrate the effectiveness and potential of the Siamese model in enhancing the performance of face matching systems. To conclude, the Siamese model is introduced as a robust and efficient tool in the field of face recognition. It provides higher accuracy and efficiency compared to the traditional feature-based models. Its adaptability and advancements bring the potential to revolutionize face-matching applications and overcome current limitations. The findings from the experiments demonstrate that the utilization of the joint model can significantly enhance the performance of the matching system. The proposed model offers a potential solution to address the issue of low accuracy during the face matching phase.