Therefore, the global approach is suitable for images of a particular class such as face images and fingerprint images.
Experiments demonstrate the effectiveness of our model for the restoration and recognition of blurred license plates as well as face images.
Nonnegative matrix factorization (NMF) has been used to cluster various data, such as face images, text corpus and gene expression data.
Existing classifier designs generally base on vector pattern, hence, when a non-vector pattern such as a face image as the input to the classifier, it has to be first concatenated to a vector.
As the face image changes from happy to angry, as the lighting changes from light to dark, or as the face turns from right to left, the coordinates of the data move along constrained sections of the ambient space the face manifold.
We refer to these fragments as well as the face images from which they were extracted as the training set.
Despite advances in equipment as well as methods, automatic face image processing for recognition or even just for the extraction of demographics, is still a challenging task in unrestricted scenarios.
However, this approach is limited in applicability as the face image must first be normalized to scale for the measurement to be accurate.
However, with the exception of 2D-PCA, images reconstructed from increasing numbers of discriminative sparse features do not resemble the original face image, as can be seen in the case of the 2D-LDA, 2D-CCA, and 2D-DGHM-based methods.
LBP has been one of the best-performing descriptors as it contains the microstructure as well as the macrostructure of the face image.
