An Improvement To The Nearest Neighbor Classifier And Face Recognition Experiments

The conventional nearest neighbor classifier (NNC) directly exploits the distances between the test sample and training samples to perform classification. NNC independently evaluates the distance between the test sample and a training sample. In this paper, we propose to use the classification procedure of sparse representation to improve NNC. The proposed method has the following basic idea: the training samples are not uncorrelated and the "distance" between the test sample and a training sample should not be independently calculated and should take into account the relationship between different training samples. The proposed method first uses a linear combination of all the training samples to represent the test sample and then exploits modified "distance" to classify the test sample. The method obtains the coefficients of the linear combination by solving a linear system. The method then calculates the distance between the test sample and the result of multiplying each training sample by the corresponding coefficient and assumes that the test sample is from the same class as the training sample that has the minimum distance. The method elaborately modifies NNC and considers the relationship between different training samples, so it is able to produce a higher classification accuracy. A large number of face recognition experiments on three face image databases show that the maximum difference between the accuracies of the proposed method and NNC is greater than 10%.