Face recognition in hyperspectral images

Pan, Zhihong; Healey, Glenn; Prasad, M. N. Giri; Tromberg, Bruce J.

doi:10.1109/tpami.2003.1251148

Cited by 316 publications

(12 citation statements)

References 23 publications

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“…While spectral signatures have little dependence on skin temperature over the NIR region, measured radiance in the thermal infrared (8 to 12 μm) has a strong dependence on skin temperature. 202 MHSI can also be limited by the cost of HSI imaging systems and by the ability to extract relevant information from large datasets.…”

Section: Discussionmentioning

confidence: 99%

Medical hyperspectral imaging: a review

2014

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Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

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Section: Discussionmentioning

confidence: 99%

Medical hyperspectral imaging: a review

2014

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“…11 All images have 31 spectral bands with center wavelengths separated by 0.01 μm over the NIR (0.7 to 1.0 μm). 11 All images have 31 spectral bands with center wavelengths separated by 0.01 μm over the NIR (0.7 to 1.0 μm).…”

Section: Methodsmentioning

confidence: 99%

“…The method to obtain the spectral reflectance from the image measurements is provided by Pan et al 11 Based on previous work 11 that showed that skin provides the best recognition results, we use skin regions for the extraction of spectral features. Several regions are chosen for feature extraction.…”

Section: Featuresmentioning

confidence: 99%

Expression-invariant face recognition in hyperspectral images

2014

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The performance of a face recognition system degrades when the expression in the probe set is different from the expression in the gallery set. Previous studies use either spatial or spectral information to address this problem. We propose an algorithm that uses spatial and spectral information for expression-invariant face recognition. The algorithm uses a set of three-dimensional Gabor filters to exploit spatial and spectral correlations, while principal-component analysis is used to model expression variation. We demonstrate the effectiveness of the algorithm on a database of 200 subjects with neutral and smiling expressions and explore the dependence of the performance on image spatial resolution and training set size.

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“…However, limited by the existing imaging techniques, higher spectral resolution often comes at the expense of lower spatial resolution. This might limit the applications of HSI in the fields of remote sensing [1,2,3], classification [4,5,6], and etc [7,8].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution with RGB Image Super-Resolution as an Auxiliary Task

Dai²,

Gool

2022

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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This work studies Hyperspectral image (HSI) superresolution (SR). HSI SR is characterized by highdimensional data and a limited amount of training examples. This raises challenges for training deep neural networks that are known to be data hungry. This work addresses this issue with two contributions. First, we observe that HSI SR and RGB image SR are correlated and develop a novel multi-tasking network to train them jointly so that the auxiliary task RGB image SR can provide additional supervision and regulate the network training. Second, we extend the network to a semi-supervised setting so that it can learn from datasets containing only low-resolution HSIs. With these contributions, our method is able to learn hyperspectral image super-resolution from heterogeneous datasets and lifts the requirement for having a large amount of high resolution (HR) HSI training samples. Extensive experiments on three standard datasets show that our method outperforms existing methods significantly and underpin the relevance of our contributions. Our code can be found at https://github.com/kli8996/HSISR.git.

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Face recognition in hyperspectral images

Cited by 316 publications

References 23 publications

Medical hyperspectral imaging: a review

Medical hyperspectral imaging: a review

Expression-invariant face recognition in hyperspectral images

Hyperspectral Image Super-Resolution with RGB Image Super-Resolution as an Auxiliary Task

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