Face recognition has many useful applications spanning surveillance, law enforcement, information security, smart card and entertainment technologies. Very recently, a learning based face recognition system is also seen to be applied to web platform combining face recognition and web service. However, many existing methods which focused on recognition accuracy cannot cope with the new social network platform because the adopted static learning approach is not adaptive to daily updated photographs among the massive number of users. In this paper, we discuss the difference between a stand-alone based system and a social network based system and propose a new collaborative face recognition framework where a redundant tagging can be avoided via sharing the identification information for efficient update under the social network platform. Our Experiments (including a web stress test) using a public database show that the proposed method records a better accuracy than that of the state-of-the-art classifier SVM adopting a polynomial kernel and has fast execution time for both training and testing.