A Proposed Framework for Identity Verification in Passport Management Using Model Scaling and Semantic Similarity

Abstract

The dependence on images for face detection and identity verification was at odds with broad assessment which showed that coordinating with sets of non-similar images was profoundly inclined to make mistakes. The current passport management lacks security of data and requires more manpower and time, manual calculations, and verification of documents as well as the photographs. Additionally, the computerized passport examination requires accurate processing in order to carry out crucial tasks like identifying passport forgeries, looking for wanted criminals, or finding those who are ineligible for immigration, among other things because the utilization of fake passports addresses a huge danger to the security of the nation. Therefore, the purpose of this paper is to enhance the identity verification process in passport management. The paper comprises implementation of various technologies and technology driven models for serving its purpose by using the latest and efficient techniques that includes neural networks, face detection, image similarity, ROI (region of interest) and model scaling. The dataset utilized in the paper was self-prepared by the authors and consisted of 5 images each for 10 different individuals. It had been trained from scratch on different models based on CNN architecture. Out of all the three models used for training, the model that resulted in highest accuracy was further considered for research. EfficientNetB0 resulted in 82% accuracy being the highest out of all the three models. The threshold for the model was thereby calculated using z-score which compared the similarity scores and classified the images as similar or non-similar. Therefore, it can be concluded that the techniques utilized in the paper are efficient enough to determine the similarity of an image with its corresponding image.