Enhanced facial recognition for thermal imagery using polarimetric imaging

Gurton, Kristan P.; Yuffa, Alex J.; Videen, Gorden

doi:10.1364/ol.39.003857

Cited by 87 publications

(51 citation statements)

References 8 publications

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“…Utilizing an emerging imaging technology that exploits polarization-state information of thermal emissions, however, it is possible to capture shape and textural details that are not present in conventional thermal imagery 6 . Polarimetric image can be represented by the Stokes parameters, S0, S1, S2, and DoLP (degree of linear polarization).…”

Section: Spectra Of Interestmentioning

confidence: 99%

Toward automated face detection in thermal and polarimetric thermal imagery

et al. 2016

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Visible spectrum face detection algorithms perform pretty reliably under controlled lighting conditions. However, variations in illumination and application of cosmetics can distort the features used by common face detectors, thereby degrade their detection performance. Thermal and polarimetric thermal facial imaging are relatively invariant to illumination and robust to the application of makeup, due to their measurement of emitted radiation instead of reflected light signals. The objective of this work is to evaluate a government off-the-shelf wavelet based naïve-Bayes face detection algorithm and a commercial off-the-shelf Viola-Jones cascade face detection algorithm on face imagery acquired in different spectral bands. New classifiers were trained using the Viola-Jones cascade object detection framework with preprocessed facial imagery. Preprocessing using Difference of Gaussians (DoG) filtering reduces the modality gap between facial signatures across the different spectral bands, thus enabling more correlated histogram of oriented gradients (HOG) features to be extracted from the preprocessed thermal and visible face images. Since the availability of training data is much more limited in the thermal spectrum than in the visible spectrum, it is not feasible to train a robust multi-modal face detector using thermal imagery alone. A large training dataset was constituted with DoG filtered visible and thermal imagery, which was subsequently used to generate a custom trained Viola-Jones detector. A 40% increase in face detection rate was achieved on a testing dataset, as compared to the performance of a pre-trained/baseline face detector. Insights gained in this research are valuable in the development of more robust multimodal face detectors.

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Section: Spectra Of Interestmentioning

confidence: 99%

Toward automated face detection in thermal and polarimetric thermal imagery

et al. 2016

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“…Recently, via an emerging technology [7], the polarization state information of thermal emission has been exploited to provide additional geometrical and textural details, especially around the nose and the mouth, which complements the textural details of the conventional intensitybased thermal images. This additional information which is not available in the conventional intensity-based thermal imaging [7], is utilized in recent algorithms to enhance the cross thermal-to-visible face recognition [5]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition

Iranmanesh

Nasrabadi

2019

2019 International Conference on Biometrics (ICB)

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In this paper, we present an attribute-guided deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. The coupled framework contains two subnetworks, one dedicated to the visible spectrum and the second sub-network dedicated to the polarimetric thermal spectrum. Each sub-network is made of a generative adversarial network (GAN) architecture. We propose a novel Attribute-Guided Coupled Generative Adversarial Network (AGC-GAN) architecture which utilizes facial attributes to improve the thermal-to-visible face recognition performance. The proposed AGC-GAN exploits the facial attributes and leverages multiple loss functions in order to learn rich discriminative features in a common embedding subspace. To achieve a realistic photo reconstruction while preserving the discriminative information, we also add a perceptual loss term to the coupling loss function. An ablation study is performed to show the effectiveness of different loss functions for optimizing the proposed method. Moreover, the superiority of the model compared to the state-ofthe-art models is demonstrated using polarimetric dataset.

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“…The work by Gurton et al [4], to the best of the authors' knowledge, is the first study in literature presenting facial imagery acquired in polarimetric LWIR, but it did not examine the contribution of polarization-state information to face recognition performance. This paper is a followup of Ref.…”

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confidence: 99%

“…This paper is a followup of Ref. [4], characterizing the facial information present in polarimetric LWIR and examining its performance for cross-modal face recognition. We demonstrate that a substantial improvement in automatic face recognition performance can be achieved by exploiting polarimetric information.…”

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Improving cross-modal face recognition using polarimetric imaging

Short

Gurram

et al. 2015

Opt. Lett.

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We investigate the performance of polarimetric imaging in the long-wave infrared (LWIR) spectrum for cross-modal face recognition. For this work, polarimetric imagery is generated as stacks of three components: the conventional thermal intensity image (referred to as S₀), and the two Stokes images, S₁ and S₂, which contain combinations of different polarizations. The proposed face recognition algorithm extracts and combines local gradient magnitude and orientation information from S₀, S₁, and S₂ to generate a robust feature set that is well-suited for cross-modal face recognition. Initial results show that polarimetric LWIR-to-visible face recognition achieves an 18% increase in Rank-1 identification rate compared to conventional LWIR-to-visible face recognition. We conclude that a substantial improvement in automatic face recognition performance can be achieved by exploiting the polarization-state of radiance, as compared to using conventional thermal imagery.

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Enhanced facial recognition for thermal imagery using polarimetric imaging

Cited by 87 publications

References 8 publications

Toward automated face detection in thermal and polarimetric thermal imagery

Toward automated face detection in thermal and polarimetric thermal imagery

Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition

Improving cross-modal face recognition using polarimetric imaging

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