Full-Reference Facial Image Quality Assessment and Identification by Two Proposed Measures

Abstract

In this paper, the goal was to identify a person’s face in the acquired image by the proposed measures. We discuss the appearance of two types of noise together in an image. The acquired facial image quality was also assessed by two proposed measures, the histogram similarity measure and the histogram error mean measure. The histogram structural similarity measure is a previously described modified version of the information-theoretic structural similarity measure. It was merged with the structural similarity measure and the error mean measure, derived from the mean squared error, to get the proposed measures. The first proposed histogram similarity measure consists of merging histogram structural similarity with structural similarity measure, and the second proposed histogram error mean measure consists of merging histogram structural similarity with error mean measure. Finally, many algorithms for identification have recently been proposed to measure the similarity between two images. The results showed that the two proposed measures were better than existing methods. Different noises types (such as white Gaussian, speckle, and salt-and-pepper) are used with the proposed methods. Two facial image datasets were used in this paper. The AT&T database included color images of 92 x 112 pixels (px), and the Faculty of Industrial Engineering database included color images of 480 x 640 px. To evaluate performance and quantify the error, the structural similarity measure, histogram structural similarity, and error mean measure were considered. Noise ratios that depended on a peak signal-to-noise ratio were used in this experiment.

Face Image Quality Assessment: A Literature Survey

“…for an image or video compression scenario [78] literature more speciically considered single-image input FIQA approaches, which implies no-reference FIQA, and means that no other data speciic to the corresponding person (or biometric capture subject [70]) is required to facilitate the FIQA. An outlier is the recent work from Dihin et al [82], which does consider multiple full-reference IQAAs for face images, for both FIQA and for FR. Note that any FR comparison method can technically fall under the deinition of full/reduced-reference (F)IQA if the comparison scores are repurposed as quality scores.…”