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
Wavelet is widely used in signal processing field. As the simplest wavelet framework, Haar wavelet is very popular due
to its memory efficient, fast and easy to implement. Haar wavelet can be employed to process the image for further
purpose like image compression. In this paper, the effectiveness of Haar wavelet and grayscale features are evaluated by
employing template matching as the principle technique. Results show that Haar wavelet feature is more relevant in
facial feature detection task as compared to grayscale feature.