Most computational methods in social interaction focus on individuals alone rather than in social context. We present an unsupervised approach to discover interpersonal synchrony, referred as to two or more persons preforming common actions in overlapping video frames or segments. For computational efficiency, we develop a branch-and-bound (B&B) approach that affords exhaustive search while guaranteeing a globally optimal solution. The proposed method is entirely general. It takes from two or more videos any multi-dimensional signal that can be represented as a histogram. We derive three novel bounding functions and provide efficient extensions, including multisynchrony detection and accelerated search, using a warmstart strategy and parallelism. We evaluate the effectiveness of our approach in human actions using the CMU Mocap dataset, spontaneous facial behaviors using group-formation task dataset and parent-infant interaction dataset.