There have recently been many methods proposed for matching face sequences in the field of face retrieval. However, most of them have proven to be inefficient in large-scale video databases because they frequently require a huge amount of computational cost to obtain a high degree of accuracy. We present an efficient matching method that is based on the face sequences (called face tracks) in large-scale video databases. The key idea is how to capture the distribution of a face track in the fewest number of low-computational steps. In order to do that, each face track is represented by a vector that approximates the first principal component of the face track distribution and the similarity of face tracks bases on the similarity of these vectors. Our experimental results from a large-scale database of 457,320 human faces extracted from 370 hours of TRECVID videos from 2004-2006 show that the proposed method easily handles the scalability by maintaining a good balance between the speed and the accuracy.