Two-dimensional nearest neighbor classifiers for face recognition

Two-dimensional feature extraction methods such as two-dimensional principal component analysis (2DPCA) and two-dimensional linear discriminant analysis (2DLDA) have been extensively studied in the past several years. Numerous experimental results demonstrate that these two-dimensional feature extraction methods are generally more efficient than and at least as effective as their one-dimensional counterparts in face recognition. However, in contrary to the large number of studies in two-dimensional feature extraction methods, studies in two-dimensional pattern classification are quite few. In this paper we propose two kinds of two-dimensional nearest neighbor classifiers and test their performance in face recognition. Extensive experimental studies conducted on four benchmark face image databases: OR, Yale, FERET, and AR demonstrate that the proposed classifiers can achieve higher recognition accuracies than the nearest neighbor classifier in general.