With the rapid growth of the social web an increasing num- ber of people started to replicate their off-line preferences and lives in an on-line environment. Consequently, the social web provides an enormous source for social network data, which can be used in both commercial and research applications. However, people often take part in multiple social network sites and, generally, they share only a selected amount of data to the audience of a specific platform. Consequently, the interlink- age of social graphs from different sources getting increasingly impor- tant for applications such as social network analysis, personalization, or recommender systems. This paper proposes a novel method to enhance available user re-identification systems for social network data aggrega- tion based on face-recognition algorithms. Furthermore, the method is combined with traditional text-based approaches in order to attempt a counter-balancing of the weaknesses of both methods. Using two sam- ples of real-world social networks (with 1610 and 1690 identities each) we show that even though a pure face-recognition based method gets out- performed by the traditional text-based method (area under the ROC curve 0.986 vs. 0.938) the combined method significantly outperforms both of these (0.998, p = 0.0001) suggesting that the face-based method indeed carries complimentary information to raw text attributes.