Due to the potential applications in facial recognition systems, face hallucination has become an active area of research.
[citation needed] Therefore, the goal of face hallucination is to make the input image reach that number of pixels.
The algorithm is based on Bayesian MAP formulation and use gradient descent to optimize the objective function and it generates the high frequency details from a parent structure with the assistance of training samples.
This method was proposed by J. Yang and H. Tang[3] and it is based in hallucinating of High-Resolution face image by taking Low-Resolution input value.
The method exploits the facial features by using a Non-negative Matrix factorization (NMF) approach to learn localized part-based subspace.
By selecting the number of "eigenfaces", we can extract amount of facial image information of low resolution and remove the noise.
The algorithm improves the image resolution by inferring some high-frequency face details from the low-frequency facial information by taking advantage of the correlation between the two parts.
For high-resolution face images, PCA can compact this correlated information onto a small number of principal components.
This algorithm formulates the face hallucination as an image decomposition problem and propose a Morphological Component Analysis (MCA)[7] based method.
Finally, facial detail information is compensated onto the estimated HT image by using the neighbour reconstruction of position-patches.