Face Hallucination Techniques: A Survey

Abstract

In several real-world scenario, the recorded pictures often have various artifacts suchlike blur, noise, varying illuminations, occlusion, etc. due to many reasons including cheap and low-resolution imaging systems, different image processing errors, and far distance of an object from the camera/sensor. The facial images captured from such low-resolution pictures make severe impacts on the performance of various systems namely human-computer interaction, speaker recognition by mouth movements, visual speech recognition, facial expression recognition, face-recognition, etc. Facial image super-resolution (or hallucination), as one of the kernels innovations in the field of computer vision and image processing, has been an engaging but challenging technique to overcome above problems. This paper provides the comprehensive survey of existing state-of-the-art and recently published face hallucination methods. Along with this, the detailed reconstruction procedure of most successful hallucination approach i.e., position-patch based super-resolution is also provided in this work. Moreover, some useful research directions are too presented at the end which may help the research community of this filed to design and develop the new face hallucination methods for providing the more efficient solution to existing problems.