Mi Amor - A Privacy-Enhanced Environment for Online Matchmaking

With the growing popularity of online dating platforms, concerns regarding user privacy and data security have become increasingly significant. In this research paper, we propose a dating web application that prioritizes user privacy while offering secure data management. The application incorporates a unique face recognition system, horoscope-based matching, compatibility percentage, and location-based filtering to help users find potential partners with ease. By employing face verification at regular intervals, the application ensures that users are personally engaged in conversations, reducing the possibility of third-party involvement and increasing transparency[2]. Furthermore, the application employs a comprehensive registration process, including face registration, to minimize fake accounts and enhance user authenticity. Users have the flexibility to customize their profiles by appending horoscopes, editing bios, and adding images[1]. The application streamlines the matching process, allowing users to double-tap to express interest and swipe left or right to view the next profile. A bookmarking feature is also provided to facilitate future interactions or changes in user actions. Notably, the application eliminates the common practice of charging users to identify who has liked their profiles, providing instant access to interested individuals and fostering prompt communication. To enhance user experience, the application employs scrolling functionality for profile browsing and empowers users with the ability to personalize the application's themes to suit their preferences[1]. Once mutual interest is established, a real-time chat messaging feature is activated, enabling users to engage in meaningful conversations and foster connections. The backend infrastructure leverages Face Net and other machine learning models to implement the proposed functionalities effectively. The process involves registering the user's face during initial setup, followed by regular face verification at 60-second intervals. To optimize storage and processing, a machine learning model is employed to extract and store only the essential features from the images, resulting in efficient data management and improved processing speed[11]