Development of Mobile Social Network Systems Using Real-Time Facial Authentication and Collaborative Recommendations

We propose a location-based mobile social networking system (SNS) that integrates biometric facial authentication using a modified local directional pattern (MLDP), collaborative content recommendations, and communication among users. The proposed system is an effective fusion of a traditional social networking system, a location-based system, and a provider of intelligent recommendations. The prototype and service scenario in this study show possibilities for technical advancement and the future extension of mobile social networking services. To verify the proposed system, we performed experiments to determine the recognition rate for a user's facial image on a real-world smartphone and the preference prediction accuracy of a collaborative filtering-based recommendation system. Through our implementation and preference prediction experiments, we confirmed that the proposed system is highly effective and applicable to convergence using a location-based service and a content recommender.