Face Recognition Using String Grammar Nearest Neighbor Technique

Abstract-Face recognition has become one of the important biometrics in many applications. However, there is a problem of collecting more than one image per person in the training data set, the so-called "one sample per person problem". Hence in this paper, we develop a face recognition system with a string grammar nearest neighbor (sgNN) to cope with the problem. We implement our system in three data sets, i.e., ORL, MIT-CBCL, and Georgia Tech databases. The recognition rates of the test data set from three databases are 88.25%, 87.50%, and 70.71%, respectively. Index Terms-face recognition, one sample per person