Face Hallucination Through KPCA

Abstract

This paper demonstrates how Kernel Principal Component Analysis (KPCA) can be used for face hallucination. Different with other KPCA-based methods, KPCA in this paper handles samples from two subspaces, namely the high-and lowresolution image spaces. As KPCA learns not only linear features but also non-linear features, it is anticipated that more detailed facial features could be synthesized. We propose a new model and give theoretical analysis on when it is applicable. Algorithm is then developed for implementation. Experiments are conducted to compare the proposed method with the existing well-known face hallucination methods in terms of visual quality and mean square error. Our results are better and encouraging.

Super-resolution: a comprehensive survey

“…Different types of learning based SR algorithms are discussed in the following sub-sections. Table 9 Reported Hallucination works [4], [57], [58], [71], [82], [85], [99], [100], [127], [133], [142], [154], [165], [187], [208], [235], [241], [250], [281], [285], [327] [340], [341], [343], [344], [354], [360], [372], [373], [379], [382], [394], [396], [400], [402], [403], [404], [409], [425], [430], [434], [435], [442], [443], [455], [456], [457], [458], [462]…”

“…In [276] and [465], a Kernel-PCA based prior that is a non-linear extension of the common PCA was embedded in a MAP method to take into account more complex correlations of human face images. In [409] again Kernel PCA but this time with RBF was used for face hallucination. In [127] PCA was again used for hallucination, but not directly for hallucination of face images, but for their features, i.e., the hallucination technique was applied to the feature vector of the LR face image to hallucinate the feature vector of the corresponding HR image, without actual reconstruction of the image.…”

“…Some of the references that have reported their results based on this database FERET [57], [58], [60], [71], [82], [99], [187], [188], [189], [191], [192], [193], [194], [208], [235], [259], [276], [310], [311], [322], [338], [339], [343], [344], [347], [400], [403], [404], [407], [425], [433], [434], [435], [442], [443], [453], [456], [465], [469], [475], [480], [522], [523], [556], [607] CAS-PEAL [409], [411], [412], [413]...…”

Super-resolution Reconstruction for Facial Images Based on Local Principal Component Analysis

“…However, this method needed a large amount of calculation, because each image in training set will be bilaterally projected to the feature space. The face hallucination method through KPCA proposed by Yan [4] from Zhongshan University in 2009 was also faced with the large calculated amount. The face hallucination algorithms based on interpolation and PCA was proposed by Shen Hua [5] from Hunan University in 2010.…”