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

Learning to Hallucinate Face Images via Component Generation and Enhancement

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methodsComment: IJCAI 2017. Project page: http://www.cs.cityu.edu.hk/~yibisong/ijcai17_sr/index.htm

Face hallucination based on nonparametric Bayesian learning

© 2015 IEEE. In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination

Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL

Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V