One of the interesting applications of computer vision is to be able to identify or detect persons in real world. This problem has been posed in the context of identifying people in television series  or in multi-camera networks . However, a common scenario for this problem is to be able to identify people among images prevalent on social networks. In this paper we present a method that aims to solve this problem in real world conditions where the person can be in any pose, profile and orientation and the face itself is not always clearly visible. Moreover, we show that the problem can be solved with as weak supervision only a label whether the person is present or not, which is usually the case as people are tagged in social networks. This is challenging as there can be ambiguity in association of the right person. The problem is solved in this setting using a latent max-margin formulation where the identity of the person is the latent parameter that is classified. This framework builds on other off the shelf computer vision techniques for person detection and face detection and is able to also account for inaccuracies of these components. The idea is to model the complete person in addition to face, that too with weak supervision. We also contribute three real-world datasets that we have created for extensive evaluation of the solution. We show using these datasets that the problem can be effectively solved using the proposed method.