Multi-spectral low-rank structured dictionary learning for face recognition

Jing, Xiao‐Yuan; Wu, Fei; Zhu, Xiaoke; Dong, Xiwei; Ma, Fei; Li, Zhiqiang

doi:10.1016/j.patcog.2016.01.023

Cited by 68 publications

(16 citation statements)

References 42 publications

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“…The compared methods have the same compressive sampling scheme, which is based on the coded aperture snapshot spectral imaging system [10] and utilises the Bernoulli random matrix as the sensing matrix. The main difference among the compared methods lies in the sparse recovery, where the wavelet [10], the feature representation base [13], the cluster sparsity field [28], the inter‐spectral multi‐layered conditional random field [29], and multi‐spectral low‐rank structured dictionary learning [30] are utilised as the representation bases, respectively, in the framework of compressive sensing. In the following, the compared methods are named ‘Wavelet’, ‘FR’, ‘CSF’, ‘IS‐MCF’, and ‘MLSDL’, respectively, for short.…”

Section: Resultsmentioning

confidence: 99%

Compressive spectral feature sensing

Wang

Zhu

2019

IET Image Processing

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Section: Resultsmentioning

confidence: 99%

Compressive spectral feature sensing

Wang

Zhu

2019

IET Image Processing

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“…In table 5, we have highlighted the results of their technique against image-set classification algorithms and Grayscale and RGB FR algorithms. Jing et al [52] use multi-view dictionary learning method for classification and apply it to all three datasets with different levels of corruption added to the pixels. We note the results for 0 pixel corruptions in Table 5.…”

Section: Quantitative Analysis Of Methods On Datasetsmentioning

confidence: 99%

An extensive review on spectral imaging in biometric systems: Challenges & advancements

Munir¹,

Khan

2019

Journal of Visual Communication and Image Representation

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Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images.

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“…Multispectral Low Rank Structured dictionary learning for face recognition [65] suggests real time applications, during acquisition of multi spectra images, high level of noise may corrupt the multi spectral image which can degrade the recognition rate. To improve the performance of multispectral images corrupted with noise, a novel approach called MSDL is used.…”

Section: Hyperspectral Face Recognition Techniquesmentioning

confidence: 99%

Current Advances in Hyperspectral Face Recognition

Qureshi¹,

Uzair²,

Zahra³

2020

Preprint

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Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and increase in the imaging speed has attracted computer vision scientists to apply hyperspectral imaging to ground based computer vision problems such as material classification, agriculture, chemistry and document image analysis. Hyperspectral imaging has also been explored for face recognition; to tackle the issues of pose and illumination variations by exploiting the richer spectral information of hyperspectral images. In this article, we present a detailed review on the potential of hyperspectral imaging for face recognition. We present hyperspectral image aquisition process and discuss key preprocessing challenges. We also discuss hyperspectral face recognition databases and techniques for feature extraction from the hyperspectral images. Potential future research directions are also highlighted

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Multi-spectral low-rank structured dictionary learning for face recognition

Cited by 68 publications

References 42 publications

Compressive spectral feature sensing

Compressive spectral feature sensing

An extensive review on spectral imaging in biometric systems: Challenges & advancements

Current Advances in Hyperspectral Face Recognition

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