An Improved Login Authenticable Model Using Memory Gallery Apps

The usage of mobile devices fulfills many purposes, including financial transactions, personal data management, and multimedia. To secure the mobile devices from un-authentication login various security measures are taken. In this article, a novel login authentication is proposed using a memory gallery application and digital image processing. The proposed method uses a set of images from the application gallery of a device and then implements digital image processing techniques to authenticate the user based on their selection of pre-defined images. The information acquired from the images of the application gallery creates a more memorable and personalized login experience. The usage of digital image processing techniques enables robust and efficient authentication, even in complex and varied lighting conditions. The proposed work is evaluated by using a dataset that contains real-world images. It yields high accuracy and robustness in login authentication thereby providing a user-friendly and personalized login experience. The proposed work postulates several advantages over traditional login authentication methods by improving security and usability. It reduces the risk of unauthorized access by malicious actors.