Deep Convolutional Neural Network Exploiting Transfer Learning for Country Recognition by Classifying Passport Cover

Nowadays, Citizen of one country is traveling to another country to settle their various needs through widespread modern transportation system. However, Passport is a worldly recognized indispensable identity document which is required for travelling internationally. Moreover, Citizen of many countries is strictly prohibited from travelling to other certain countries. So, Passport inspection is a key responsibility for immigration officers in order to confirm the identity of traveler. In addition to that it is a laborious and time-consuming task for immigration officers to check all passports meticulously. Hence, automatic country recognition from passport cover image can save a lot of time and physical labour by identifying those unauthorized travelers. Thus in this paper, we have investigated an automatic system using Deep Convolutional Neural Network (DCNN) based on transfer learning with Support Vector Machine (SVM) classifier to analyze passport cover for country identification. Here, the Inception-ResNet-v2 DCNN architecture has been retrained with 80% of image dataset which includes ten classes of passport cover of ten countries using transfer learning method for feature extraction and the extracted feature were then used to train SVM. The proposed model achieved an accuracy level around of 98.75% on the test image dataset.