Efficient face recognition with a large database
Face recognition has a wide range of applications, and most of the current face recognition algorithms can achieve a high level of accuracy. However, when the face database is very large, the amount of computation required to search the faces in that database becomes an important concern. In this paper, we propose to use a number of vantage objects to form an efficient indexing structure for searching a huge database. These vantage objects are constructed using the discriminative features extracted from Gabor wavelets. The training faces in the database are ranked either in ascending or descending order with reference to each of the vantage objects, and hence each vantage object can form one or more ranked lists. With a query face, it is ranked with reference to each vantage object, and is positioned in each of the ranked lists accordingly. Then, the neighboring training faces to the query face in the respective ranked lists are selected to form a much smaller database, which is called a condensed database. Experimental results show that, with a database of more than 2000 distinct faces, the probabilities of a query face being selected in a condensed database of 36%, 26%, 11%, and 6% of the original database size are 99%, 98%, 95%, and 90%, respectively. To search a face in the much smaller condensed database, a more computational and accurate recognition algorithm can then be adopted.