Estimation of visible spectrum faces from polarimetric thermal faces

Riggan, Benjamin S.; Short, Nathanial J.; Hu, Shuowen; Kwon, Heesung

doi:10.1109/btas.2016.7791170

Cited by 32 publications

(62 citation statements)

References 25 publications

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“…Feature-based Synthesis: Chen and Ross [2] demonstrated that a VIS image could be reconstructed from a THM image using hidden factor analysis. Riggan et al [19] performed VIS image reconstruction using a two-stage process, consisting of feature extraction and feature regression using CNNs. However, these reconstructed results were observed to be blurry.…”

Section: Related Workmentioning

confidence: 99%

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Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network

Chen

Ross

2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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Designing face recognition systems that are capable of matching face images obtained in the thermal spectrum with those obtained in the visible spectrum is a challenging problem. In this work, we propose the use of semantic-guided generative adversarial network (SG-GAN) to automatically synthesize visible face images from their thermal counterparts. Specifically, semantic labels, extracted by a face parsing network, are used to compute a semantic loss function to regularize the adversarial network during training. These semantic cues denote high-level facial component information associated with each pixel. Further, an identity extraction network is leveraged to generate multi-scale features to compute an identity loss function. To achieve photo-realistic results, a perceptual loss function is introduced during network training to ensure that the synthesized visible face is perceptually similar to the target visible face image. We extensively evaluate the benefits of individual loss functions, and combine them effectively to learn the mapping from thermal to visible face images. Experiments involving two multispectral face datasets show that the proposed method achieves promising results in both face synthesis and cross-spectral face matching.

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Section: Related Workmentioning

confidence: 99%

“…The ARL dataset consists of thermal and visible face images captured from 60 subjects [6]. According to the protocol used in [19], images of 60 subjects are used for the experiment. The interocular distance in these images is 87 pixels.…”

Section: Evaluation On Arl Datasetmentioning

confidence: 99%

Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network

Chen

Ross

2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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“…It has been shown that polarimetric thermal imaging captures additional geometric and textural facial details compared to conventional thermal imaging [10]. Hence, the polarization-state information has been used to improve the performance of cross-spectrum face recognition [10,27,30,35,26,5]. A polarimetric, referred to as Stokes images, is composed of three channels: S0, S1 and S2.…”

Section: Introductionmentioning

confidence: 99%

“…The large domain discrepancy between these images makes the cross-spectrum matching problem very challenging. Various methods have been proposed in the literature for crossspectrum matching [10,27,30,35,26,16,21,18,2,28,24].…”

Section: Introductionmentioning

confidence: 99%

“…Various synthesis-based methods have also been proposed in the literature [26,35,38,27] to perform crossmodal mapping at the image level for direct use in existing visible-based matchers. Riggan et al [27] trained a regression network to estimate the mapping between features from both visible and thermal then reconstruct the visible face based on the estimated features. Zhang et al [35,36] leveraged generative adversarial networks (GANs) to synthesize visible images from polarimetric thermal images.…”

Section: Introductionmentioning

confidence: 99%

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Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis

Riggan

et al. 2019

2019 International Conference on Biometrics (ICB)

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Polarimetric thermal to visible face verification entails matching two images that contain significant domain differences. Several recent approaches have attempted to synthesize visible faces from thermal images for cross-modal matching. In this paper, we take a different approach in which rather than focusing only on synthesizing visible faces from thermal faces, we also propose to synthesize thermal faces from visible faces. Our intuition is based on the fact that thermal images also contain some discriminative information about the person for verification. Deep features from a pre-trained Convolutional Neural Network (CNN) are extracted from the original as well as the synthesized images. These features are then fused to generate a template which is then used for verification. The proposed synthesis network is based on the self-attention generative adversarial network (SAGAN) which essentially allows efficient attention-guided image synthesis. Extensive experiments on the ARL polarimetric thermal face dataset demonstrate that the proposed method achieves state-of-the-art performance.

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Thermal Face Recognition Based on Transformation by Residual U-Net and Pixel Shuffle Upsampling

Chatterjee

Chu

2019

MultiMedia Modeling

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Estimation of visible spectrum faces from polarimetric thermal faces

Cited by 32 publications

References 25 publications

Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network

Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network

Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis

Thermal Face Recognition Based on Transformation by Residual U-Net and Pixel Shuffle Upsampling

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