Locality-constraint Representation with Minkowski distance metric for an effective Face Hallucination

Abstract

Face hallucination (FaceH), the domain specific super resolution technique has significant importance in the field of image processing and computer vision. These methods provide visually convincing high-resolution face images using the low-resolution input images. To do this, all the state-of-the-art methods make use of distance measure algorithms. The performance of distance measure is one of the core components that impact the performance of FaceH. However, there are many k-NN distance measure algorithms, to the best of our knowledge, the most commonly used metric is the Euclidean distance, though it is the best, a little deviation may result in large elevation on estimating the reconstructed weight of the pixel. To overcome this, it is necessary to find an alternative method and this motivated to develop Minkowski Locality-constrained Representation (MinLcR) FaceH that makes use of Minkowski distance and after which it is integrated with Locality-constrained Representation (LcR) that incorporates the contextual information of the neighboring pixel for better hallucination. The main idea of MinLcR is to make a difference in the distance metric and compare it with methods that make use of Euclidean distance. Experimental analysis carried out using FEI database shows that the proposed method has an effective improvement over other methods.