A KPLS-Eigentransformation Model Based Face Hallucination Algorithm

The traditional eigentransformation method for face hallucination is a linear subspace approach, which represents an image as a linear combination of training samples. Consequently, those novel facial appearances not included in the training samples cannot be superresolved properly. In this paper, a KPLS (Kernel partial least squares) regression is introduced into the eigentransformation method to reconstruct the High resolution (HR) image from a Low resolution (LR) facial image. We have evaluated our proposed method using different zooming factors and compared these performances with the current Super resolution (SR) algorithms. Experimental results show that our algorithm can produce better HR face images than the compared eigentransformation based method and the KPLS method in terms of both visual quality and numerical errors.