ICA Based Super-Resolution Face Hallucination and Recognition

Yan, Hua; Liu, Ju; Sun, Jiande; Sun, Xinghua

doi:10.1007/978-3-540-72393-6_126

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…For imposing global constraints, some SR algorithms have used projectionbased methods for learning the a priori term of the employed MAP algorithm, e.g., in [85] (2001), [142], [347], [400], [425], [442], [443], [452], [453], [457], [458], [465], [467], [504] PCA in [323], [349], [502], [525] Independent Component Analysis (ICA), and in [472] Morphological Component Analysis (MCA) have been used. In PCA every face is represented by its PC basis, which is computed from training images at the desired resolution f = V y − µ, where V is the set of PC basis vectors and µ is the average of the training images [85] (2001).…”

Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

“…- [189], [190], [213], [298], [299], [322], [323], [338], [339], [349], [433], [453], [469], [480], [510], [537], [133], [150], [151], [179], [160], [162], [174], [207], [209], [210], [215], [216], [217], [223], [231], [237], [241], [247], [251], [252], [273], [281], [285], [345], [308], [309], [317], [323], [326], [331], [344], [349], [353], [36...…”

Section: Assessment Of Sr Algorithmsunclassified

“…Among the most common databases reviewed (shown in Table 15), only CAS-PEAL facial database provides such a data. Beside the list shown in Table 14, the following facial databases have been used in at least one system: Cohn-Kanade face database [208], NUST [323], [349], BioID [394], [400], Terrascope [299], Asian Face Database PF01 [327], FG-NET Database [341], Korean Face Database [355], Max Planck Institute Face Database [355], IMM Face Database [382], PAL [396], [455], USC-SIPI [489], [503], [577], Georgia Tech [407], [523], ViD-TIMIT [400], FRI CVL [480], Face96 [480], FEI face database [571], MBGC face and iris database [585], SOFTPIA Japan Face Database [605].…”

Section: Databasesmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new superresolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

Section: Assessment Of Sr Algorithmsunclassified

Section: Databasesmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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“…In order to synthesis the high-resolution (HR) image from the low-resolution (LR) image, the computation can be performed in two manners: reconstruction-based [3], [4], [5] and learning-based [2], [7], [9], [11]. In this paper, we focus on the second approach which applied to human face images, is also known as face hallucination [1].…”

Section: Introductionmentioning

confidence: 99%

“…In [2], PCA was utilized as feature extraction and MAP estimation framework is incorporated to explore the underlying statistical structure of the face images. In [11], Independent Component Analysis (ICA) was used to build a linear mixing relationship between HR face image and independent HR source faces images. All above methods, each 2D face image matrix must be previously transformed into vector and then a collection of the transformed face vectors are concatenated into a matrix.…”

Section: Introductionmentioning

confidence: 99%

Face Hallucination Using Bilateral-Projection-Based Two-Dimensional Principal Component Analysis

Sanguansat

2008

2008 International Conference on Computer and Electrical Engineering

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In this paper, we propose a new super-resolution face hallucination method based on Bilateral-projection-based TwoDimensional Principal Component Analysis (B2DPCA). Firstly, the high-resolution (HR) face image and its corresponding low-resolution (LR) face image are projected to the HR and LR B2DPCA feature spaces, respectively. In these spaces, the linear mixing relationship between HR and LR feature is estimated from a training set. For reconstructing the HR image from the observed LR image, the LR image is firstly projected to LR feature space and then mapped to HR feature. Finally, the HR feature is reconstructed to the HR face image. Experiments on the well-known face databases show that the performance of our proposed method. The resolution and quality of the hallucinated face images are greatly enhanced over the LR ones, which is very helpful for human recognition.

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Finding and improving the key-frames of long video sequences for face recognition

Nasrollahi

Moeslund

2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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ICA Based Super-Resolution Face Hallucination and Recognition

Cited by 4 publications

References 3 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Face Hallucination Using Bilateral-Projection-Based Two-Dimensional Principal Component Analysis

Finding and improving the key-frames of long video sequences for face recognition

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