A unified framework for subspace based face recognition.

Wang Xiaogang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 88-91).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.vTable of Contents --- p.viList of Figures --- p.viiiList of Tables --- p.xChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Face recognition --- p.1Chapter 1.2 --- Subspace based face recognition technique --- p.2Chapter 1.3 --- Unified framework for subspace based face recognition --- p.4Chapter 1.4 --- Discriminant analysis in dual intrapersonal subspaces --- p.5Chapter 1.5 --- Face sketch recognition and hallucination --- p.6Chapter 1.6 --- Organization of this thesis --- p.7Chapter Chapter 2 --- Review of Subspace Methods --- p.8Chapter 2.1 --- PCA --- p.8Chapter 2.2 --- LDA --- p.9Chapter 2.3 --- Bayesian algorithm --- p.12Chapter Chapter 3 --- A Unified Framework --- p.14Chapter 3.1 --- PCA eigenspace --- p.16Chapter 3.2 --- Intrapersonal and extrapersonal subspaces --- p.17Chapter 3.3 --- LDA subspace --- p.18Chapter 3.4 --- Comparison of the three subspaces --- p.19Chapter 3.5 --- L-ary versus binary classification --- p.22Chapter 3.6 --- Unified subspace analysis --- p.23Chapter 3.7 --- Discussion --- p.26Chapter Chapter 4 --- Experiments on Unified Subspace Analysis --- p.28Chapter 4.1 --- Experiments on FERET database --- p.28Chapter 4.1.1 --- PCA Experiment --- p.28Chapter 4.1.2 --- Bayesian experiment --- p.29Chapter 4.1.3 --- Bayesian analysis in reduced PCA subspace --- p.30Chapter 4.1.4 --- Extract discriminant features from intrapersonal subspace --- p.33Chapter 4.1.5 --- Subspace analysis using different training sets --- p.34Chapter 4.2 --- Experiments on the AR face database --- p.36Chapter 4.2.1 --- "Experiments on PCA, LDA and Bayes" --- p.37Chapter 4.2.2 --- Evaluate the Bayesian algorithm for different transformation --- p.38Chapter Chapter 5 --- Discriminant Analysis in Dual Subspaces --- p.41Chapter 5.1 --- Review of LDA in the null space of and direct LDA --- p.42Chapter 5.1.1 --- LDA in the null space of --- p.42Chapter 5.1.2 --- Direct LDA --- p.43Chapter 5.1.3 --- Discussion --- p.44Chapter 5.2 --- Discriminant analysis in dual intrapersonal subspaces --- p.45Chapter 5.3 --- Experiment --- p.50Chapter 5.3.1 --- Experiment on FERET face database --- p.50Chapter 5.3.2 --- Experiment on the XM2VTS database --- p.53Chapter Chapter 6 --- Eigentransformation: Subspace Transform --- p.54Chapter 6.1 --- Face sketch recognition --- p.54Chapter 6.1.1 --- Eigentransformation --- p.56Chapter 6.1.2 --- Sketch synthesis --- p.59Chapter 6.1.3 --- Face sketch recognition --- p.61Chapter 6.1.4 --- Experiment --- p.63Chapter 6.2 --- Face hallucination --- p.69Chapter 6.2.1 --- Multiresolution analysis --- p.71Chapter 6.2.2 --- Eigentransformation for hallucination --- p.72Chapter 6.2.3 --- Discussion --- p.75Chapter 6.2.4 --- Experiment --- p.77Chapter 6.3 --- Discussion --- p.83Chapter Chapter 7 --- Conclusion --- p.85Publication List of This Thesis --- p.87Bibliography --- p.8

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202

Face hallucination under an image decomposition perspective

In this paper we propose to convert the task of face hallucination into an image decomposition problem, and thenuse the morphological component analysis (MCA) for hallucinating a single face image, based on a novel three-stepframework. Firstly, a low-resolution input image is up-sampled by interpolation. Then, the MCA is employed to decompose the interpolated image into a high-resolution image and an unsharp masking, as MCA can properly decompose a signal into special parts according to typical dictionaries. Finally, a residue compensation, which is based on the neighbour reconstruction of patches, is performed to enhance the facial details. The proposed method can effectively exploit the facial properties for face hallucination under the image decomposition perspective. Experimental results demonstrate the effectiveness of our method, in terms of the visual quality of the hallucinated face images