Expression-invariant face recognition in hyperspectral images

Wang, Han; Bau, Tien C.; Healey, Glenn

doi:10.1117/1.oe.53.10.103102

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…Wang et al [50] proposed Gabor features for expression invariant face recognition. They acquired dataset of 400 images from 200 subjects over 31 bands of Near Infra Red (NIR) from 0.7 µm to 1.0 1.00 µm.…”

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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Section: Hyperspectral Face Recognition Techniquesmentioning

confidence: 99%

Current Advances in Hyperspectral Face Recognition

Qureshi¹,

Uzair²,

Zahra³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous studies have shown that spectral information can be used for face recognition [21][22][23][24][25][26][27][28][29][30]. Pan et al proposed a method that uses spectral signatures in the near-infrared (near-IR) to overcome variation in expression, pose, and illumination [21,22,26].…”

Section: Journal Of Computer Sciences and Applicationsmentioning

confidence: 99%

“…The near-IR range was chosen because it has a larger penetration depth than for visible radiation which makes near-IR characteristics difficult for a subject to modify [33]. These works [21][22][23][24][25][26][27][28][29][30] showed that spectral signatures are stable for a person and are different from person-to-person which makes these signatures useful for recognition. Near-IR images also provide spatial information that can be exploited.…”

Section: Journal Of Computer Sciences and Applicationsmentioning

confidence: 99%

“…Methods based on illumination-invariant features have also been proposed [5][6][7][8][9][10][11][12][13][14]. On the other hand, previous studies have shown that spectral information is useful for this purpose [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. However, existing approaches use either spatial or spectral information.…”

Section: Introductionmentioning

confidence: 99%

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Illumination-Invariant Face Recognition in Hyperspectral Images

Wang¹,

Healey²

2019

JCSA

Self Cite

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Illumination-invariant face recognition remains a challenging problem. Previous studies use either spatial or spectral information to address this problem. In this paper, we propose an algorithm that uses spatial and spectral information simultaneously. We first learn a basis in the spectral domain. We then extract spatial features using 2D Gabor filters. Finally, we use the basis and the spatial features to classify face images. We demonstrate the effectiveness of the algorithm on a database of 200 subjects.

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“…In [15], Sharma and Gool considered each band in the hyperspectral images as a separate image and they claimed that this approach helps exploiting higherlevel information. For expression-invariant face recognition, Wang et al [16] employed a set of 3D Gabor filters to extract spatial and spectral information and utilised PCA to model expression variation.…”

Section: Introductionmentioning

confidence: 99%

3D discrete wavelet transform‐based feature extraction for hyperspectral face recognition

Ghasemzadeh

Demirel

2017

IET biom.

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Facial hyperspectral image analysis has become a popular topic since it provides additional spectral information on subjects unlike the 2D face imagery which only has spatial information, hence it has an opportunity to improve face recognition accuracy. Three new methods for feature extraction for facial hyperspectral image classification are proposed. The methods employ a three-dimensional discrete wavelet transform (3D-DWT) to extract features from facial hyperspectral images. One of the advantages of 3D-DWT for feature extraction in hyperspectral images is that the horizontal, vertical and spectral information are processed in parallel. The most important characteristic of 3D-DWT is decomposing hyperspectral images into a set of spatio-spectral frequency subbands. The study proposes three methods using 3D-DWT for feature extraction: 3D-subband energy, 3D-subband overlapping cube and 3D-global energy. The k-NN and collaborative representation-based classifier (CRC) are used to process extracted feature vector datasets, where classification accuracies are evaluated by four test scenarios. The results under different test scenarios revealed that accuracy of proposed 3D-DWT methods is superior to alternative methods using spatio-spectral classification.

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Expression-invariant face recognition in hyperspectral images

Cited by 12 publications

References 8 publications

Current Advances in Hyperspectral Face Recognition

Current Advances in Hyperspectral Face Recognition

Illumination-Invariant Face Recognition in Hyperspectral Images

3D discrete wavelet transform‐based feature extraction for hyperspectral face recognition

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