In this paper, we present a method for agglomerative clustering of characters in a video. Given a video edited with humans, we seek to identify each person with the character they represent. The proposed method is based on agglomerative clustering of deep face features, using first neighbour relations. First, the heads and faces of each person are detected and tracked in each shot of the video. Then, we create a feature vector of a tracked person in a shot. Finally, we compare the feature vectors and we use first neighbour relations to group them into distinct characters. The main contribution of this work is a person re-identification framework based on an agglomerative clustering method, and applied to edited videos with large scene variations.