2DPCA-based row-kNN distance computation for face recognition
Waled Hussein Al-Arashi, Shahrel Azmin Suandi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Since two-dimensional principal component analysis has been used in face recognition, many approaches in 2D-based method have been developed. However, less attention is spent in the classification methods based on 2D image matrix. Considering that the feature extracted from 2DPCA based is a matrix instead of a single vector as in PCA based, a new measurement distance is proposed which considers the rows of the feature matrix. Unlike the previous methods which are depending on the columns or the whole matrix of the feature matrix, the proposed method is combined with the k-nearest neighbour instead of the 1-nearest neighbour. Moreover, by using the proposed method, the drawback of 2DPCA based algorithms compared to PCA based algorithms, which is the increment of the coefficient numbers, can be alleviated. Experimental results on a famous face databases show that by increasing the number of training images per class, the proposed method accuracy is also increased until it surpasses all methods in terms of accuracy and storage capacity.