Low resolution face recognition using combination of diverse classifiers

This paper presents an appropriate solution for low resolution faces recognition problem, using combination of diverse classifiers. We investigate our model based on extracting important features from low resolution images using three well known feature extractors; PCA, DCT and FFT, assigning MLP classifiers to each feature extractor and combining the votes of MLP classifiers using fusion of experts techniques. The results show that using the combination of three mentioned feature extractors and applying the Stack Generalization as combiner of classifiers for low resolution face recognition task, leads to higher performance than other recognition models. It's worth nothing that using a single feature extractor for low resolution face recognition task was failed in the previously surveys.