Blur Insensitive Texture Classification Using Local Phase Quantization

Ojansivu, Ville; Heikkilä, Janne

doi:10.1007/978-3-540-69905-7_27

Cited by 804 publications

(475 citation statements)

References 11 publications

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“…This step is critical to the performance of the recognition system. The commonly-used characteristics are: Gabor coefficients [66], Haar wavelets [67], Fourier transforms, scale-invariant feature transform (SIFT) [68], the characteristics based on the local binary pattern method (LBP) [69], local phase quantization (LPQ) [70], Weber law descriptor (WLD) [71] and binarized statistical image features (BSIF) [72].…”

Section: Local Appearance-based Face Recognition Methodsmentioning

confidence: 99%

A Survey of 2D Face Recognition Techniques

et al. 2016

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Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method's principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.

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Section: Local Appearance-based Face Recognition Methodsmentioning

confidence: 99%

A Survey of 2D Face Recognition Techniques

et al. 2016

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“…In this paper, contrary from designing of ideal filters for preprocessing the ambiguous SAR images, we considered extracting the ambiguous invariant local phase feature in frequency domain [30][31][32][33]. For SAR images, ambiguity of the observed image is inevitable due to the existence of range ambiguities and azimuth ambiguities.…”

Section: Frequency Domain Feature Extraction Based On Local Phase Quamentioning

confidence: 99%

A Multi-Scale Local Phase Quantization Plus Biomimetic Pattern Recognition Method for Sar Automatic Target Recognition

Zhai¹,

Li²,

Gan³

et al. 2013

PIER

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Abstract-Synthetic aperture radar (SAR) automatic target recognition (ATR) has been receiving more and more attention in the past two decades. But the problem of how to overcome SAR target ambiguities and azimuth angle variations has still left unsolved. In this paper, a multi-scale local phase quantization plus biomimetic pattern recognition (BPR) method is presented to solve these two difficulties. By applying multiple scales local phase quantization (LPQ) on the observed SAR images, the blur and azimuth invariant features can be extracted, and these features are fusion at consecutive multiple scales to achieve better results. Then PCA method is applied to further reduce the feature dimension and achieve its efficiency. Finally, high dimensional space geometry covering method based on BPR theory is adopted to construct hyper sausage neuron links for target recognition. Experiments on the MSTAR database show that the proposed method can achieve satisfying recognition accuracy compared with other stateof-the-art methods.

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“…Face recognition technology has made an immense progress during the last decade, first thanks to the advances in face representation in the form of innovative features such as Local Binary Patterns (LBP) [38,39], Local Phase Quantisation (LPQ) [6,40] and Binarised Statistical Image Features (BSIF) [29], 5 and more recently, to the capabilities of end-to-end deep learning neural networks [51,53]. As a result, for near frontal faces, captured in reasonable environmental conditions, the reported recognition rates match or exceed human performance [33,53].…”

Section: Introductionmentioning

confidence: 99%

Gaussian mixture 3D morphable face model

Koppen

Feng

Kittler

et al. 2018

Pattern Recognition

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Abstract3D Morphable Face Models (3DMM) have been used in pattern recognition for some time now. They have been applied as a basis for 3D face recognition, as well as in an assistive role for 2D face recognition to perform geometric and photometric normalisation of the input image, or in 2D face recognition system training. The statistical distribution underlying 3DMM is Gaussian. However, the single-Gaussian model seems at odds with reality when we consider different cohorts of data, e.g. Black and Chinese faces. Their means are clearly different. This paper introduces the Gaussian Mixture 3DMM (GM-3DMM) which models the global population as a mixture of Gaussian subpopulations, each with its own mean. The proposed GM-3DMM extends the traditional 3DMM naturally, by adopting a shared covariance structure to mitigate small sample estimation problems associated with data in high dimensional spaces. We construct a GM-3DMM, the training of which involves a multiple cohort dataset, SURREY-JNU, comprising 942 3D face scans of people with mixed backgrounds. Experiments in fitting the GM-3DMM to 2D face images to facilitate their geometric and photometric normalisation for pose and illumination invariant face recognition demonstrate the merits of the proposed mixture of Gaussians 3D face model.

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Blur Insensitive Texture Classification Using Local Phase Quantization

Cited by 804 publications

References 11 publications

A Survey of 2D Face Recognition Techniques

A Survey of 2D Face Recognition Techniques

A Multi-Scale Local Phase Quantization Plus Biomimetic Pattern Recognition Method for Sar Automatic Target Recognition

Gaussian mixture 3D morphable face model

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