Employing Domain Specific Discriminative Information to Address Inherent Limitations of the LBP Descriptor in Face Recognition

Fan, Junjie; Arandjelović, Ognjen

doi:10.1109/ijcnn.2018.8489691

Cited by 7 publications

(5 citation statements)

References 43 publications

(43 reference statements)

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“…A local binary pattern (LBP) is a descriptor originally proposed to describe local appearance in an image. The key idea behind it is that the relative brightness of neighbouring pixels can be used to describe local appearance in a geometrically and photometrically robust manner [14][15][16]. The basic LBP feature extractor relies on two free parameters, call them R and P. Uniformly sampling P points on the circumference of a circle with the radius R centred at a pixel, and taking their brightness relative to the centre pixel (brighter than, or not-one bit of information) allows the neighbourhood to be characterized by a P-bit number.…”

Section: Local Binary Pattern-three Orthogonal Planes (Lbp-top)mentioning

confidence: 99%

Review of Automatic Microexpression Recognition in the Past Decade

Zhang

Arandjelović

2021

MAKE

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Facial expressions provide important information concerning one’s emotional state. Unlike regular facial expressions, microexpressions are particular kinds of small quick facial movements, which generally last only 0.05 to 0.2 s. They reflect individuals’ subjective emotions and real psychological states more accurately than regular expressions which can be acted. However, the small range and short duration of facial movements when microexpressions happen make them challenging to recognize both by humans and machines alike. In the past decade, automatic microexpression recognition has attracted the attention of researchers in psychology, computer science, and security, amongst others. In addition, a number of specialized microexpression databases have been collected and made publicly available. The purpose of this article is to provide a comprehensive overview of the current state of the art automatic facial microexpression recognition work. To be specific, the features and learning methods used in automatic microexpression recognition, the existing microexpression data sets, the major outstanding challenges, and possible future development directions are all discussed.

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Section: Local Binary Pattern-three Orthogonal Planes (Lbp-top)mentioning

confidence: 99%

Review of Automatic Microexpression Recognition in the Past Decade

Zhang

Arandjelović

2021

MAKE

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“…Finally, it demonstrated an outstanding performance (92.8%) for the UWA-HSFD. Also, Fan et al [33] investigated the existing limitations of using LBP to recognise the face. Then the gradient-weighted strategy was introduced to adjust regional histograms, developing the smoothness effect in terms of the discriminative contributions of the local positions.…”

Section: Related Research Workmentioning

confidence: 99%

Hyperspectral face recognition with a spatial information fusion for local dynamic texture patterns and collaborative representation classifier

Hao

Liu

Xie³

2021

IET Image Processing

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Hyperspectral face recognition provides improved classification rates due to its abundant information in the face cubes of every subject in hyperspectral face databases. However, while offering excellent opportunities, it also brings new challenges, such as low signal-tonoise ratio, interband misalignment, and high data dimensionality. Based on these ad hoc problems, literature has already proposed some optimisation methods including dimensionality reduction, image denoising, and alignment to perform face recognition, yet lacking comprehensive evaluation. This paper proposes a novel hyperspectral face recognition algorithm that is based on spatial information fusion for feature extraction (histogram of local dynamic texture patterns) and collaborative representation classifier for classification. Meanwhile, the algorithm is applied to three popular hyperspectral face databases, Carnegie Mellon University (CMU)-hyperspectral face database (HSFD), University of Western Australia (UWA)-HSFD, and Hong Kong Polytechnic University (PolyU)-HSFD databases. Experimental results demonstrate that CMU-HSFD and UWA-HSFD databases achieve very competitive classification results. PolyU-HSFD database also achieves rather good classification rates. The best recognition results are 98.5% ± 0.95, 96.6% ± 0.98, and 94.0% ± 2.86 for CMU-HSFD, UWA-HSFD and PolyU-HSFD, respectively. It demonstrates experimentally that this algorithm can be used to recognise faces. Moreover, we compared eight existing state-of-the-art face recognition techniques with our proposed method in performing hyperspectral face recognition. In this research, we formulate hyperspectral face recognition as an image-set classification problem and evaluate the performances compared with other kinds of algorithms. Comparisons with the eight existing hyperspectral face recognition techniques on three standard datasets show that the proposed algorithm outperforms most other state-of-the-art algorithms, indicating that it is a promising approach for hyperspectral face recognition.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The stages are: Feature extraction The data in the given video frame is processed to obtain information useful for detection and tracking. This could involve analysing low-level features [ 17 , 18 ]—such as colour or brightness—to detect edges or interest points. Colour layout descriptors (CLDs) and histograms of oriented gradients (HOGs) [ 19 ] are examples of feature detectors.…”

Section: Previous Workmentioning

confidence: 99%

Tracking of Deformable Objects Using Dynamically and Robustly Updating Pictorial Structures

Ratcliffe

Arandjelović

2020

J. Imaging

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The problem posed by complex, articulated or deformable objects has been at the focus of much tracking research for a considerable length of time. However, it remains a major challenge, fraught with numerous difficulties. The increased ubiquity of technology in all realms of our society has made the need for effective solutions all the more urgent. In this article, we describe a novel method which systematically addresses the aforementioned difficulties and in practice outperforms the state of the art. Global spatial flexibility and robustness to deformations are achieved by adopting a pictorial structure based geometric model, and localized appearance changes by a subspace based model of part appearance underlain by a gradient based representation. In addition to one-off learning of both the geometric constraints and part appearances, we introduce a continuing learning framework which implements information discounting i.e., the discarding of historical appearances in favour of the more recent ones. Moreover, as a means of ensuring robustness to transient occlusions (including self-occlusions), we propose a solution for detecting unlikely appearance changes which allows for unreliable data to be rejected. A comprehensive evaluation of the proposed method, the analysis and discussing of findings, and a comparison with several state-of-the-art methods demonstrates the major superiority of our algorithm.

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Employing Domain Specific Discriminative Information to Address Inherent Limitations of the LBP Descriptor in Face Recognition

Cited by 7 publications

References 43 publications

Review of Automatic Microexpression Recognition in the Past Decade

Review of Automatic Microexpression Recognition in the Past Decade

Hyperspectral face recognition with a spatial information fusion for local dynamic texture patterns and collaborative representation classifier

Tracking of Deformable Objects Using Dynamically and Robustly Updating Pictorial Structures

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