Dissimilarity-based nearest neighbor classifier for single-sample face recognition

In single-sample face recognition (SSFR) tasks, the nearest neighbor classifier (NNC) is the most popular method for its simplicity in implementation. However, in complex situations with light, posture, expression, and obscuration, NNC cannot achieve good recognition performance when applying common distance measurements, such as the Euclidean distance. Thus, this paper proposes a novel distance measurement scheme for NNC and applies it to SSFR. The proposed method, called dissimilarity-based nearest neighbor classifier (DNNC), first segments each (training or test) image into non-overlapping patches with a given size and then generates an ordered image patch set. The dissimilarities between the given test image patch set and the training image patch sets are computed and taken as the distance measurement of NNC. The smaller the dissimilarity of image patch sets is, the closer is the distance from the test image to the training image. Therefore, the category of the test image can be determined according to the smallest dissimilarity. Extensive experiments on the AR face database demonstrate the effectiveness of DNNC, especially for the case of obscuration.