Fast Screening in Large Face Databases Using Merit-Based Dominant Points

Current face identification approaches require computer systems to search through large quantity of face feature sets in the database and pick the ones that best match the features of an unknown input face. In this paper, a fast screening method for large face database searching is proposed. The method utilizes dominant points instead of edge maps as features for similarity measurement. A new formulation of Hausdorff distance is designed for merit-based dominant point matching. The screening experiments demonstrated that the proposed face screening method significantly improves the computational speed and the storage economy. It provides a very efficient way for large face databases searching and screening.