TR-GAN: thermal to RGB face synthesis with generative adversarial network for cross-modal face recognition

Kezebou, Landry; Oludare, Victor; Panetta, Karen; Agaian, Sos S.

doi:10.1117/12.2558166

Cited by 20 publications

(19 citation statements)

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“…Moreover, using a mapping function, it maximizes the conditional probability. TR-GAN [91] was proposed as a cross model over thermal to RGB. Here, GAN is used for loss training, and the generator part synthesizes images with fine details.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Recent Advances in Deep Learning Techniques for Face Recognition

et al. 2021

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In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 171 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks. INDEX TERMSDeep learning, face recognition, artificial neural network, convolutional neural network, auto encoder, generative adversarial network, deep belief network, reinforcement learning.

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Section: Generative Adversarial Networkmentioning

confidence: 99%

Recent Advances in Deep Learning Techniques for Face Recognition

et al. 2021

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“…Apart from formulating new loss functions, some researchers have designed novel network architectures for the generator such as a densely connected encoder-decoder structure [10], cascaded-in-cascaded blocks [12], and selfattention blocks [3], that enable generating higher quality images. Iranmanesh et al [10] presented a coupled generative adversarial network (CpGAN) architecture that incorporated a densely connected encoder-decoder structure in the generator.…”

Section: Related Workmentioning

confidence: 99%

“…Iranmanesh et al [10] presented a coupled generative adversarial network (CpGAN) architecture that incorporated a densely connected encoder-decoder structure in the generator. Kezebou et al [12] proposed to reuse features from earlier convolutional layers via a UNET-like architecture with cascaded-in-cascaded blocks. Di et al [3] enhanced a GAN with self-attention modules to enable attention-guided image synthesis.…”

Section: Related Workmentioning

confidence: 99%

Explainable Thermal to Visible Face Recognition Using Latent-Guided Generative Adversarial Network

Anghelone

Chen²,

Fauré³

et al. 2021

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

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One of the main challenges in performing thermalto-visible face image translation is preserving the identity across different spectral bands. Existing work does not effectively disentangle the identity from other confounding factors. In this paper, we propose a Latent-Guided Generative Adversarial Network (LG-GAN) to explicitly decompose an input image into identity code that is spectral-invariant and style code that is spectral-dependent. By using such a disentanglement, we are able to analyze the identity preservation by interpreting and visualizing the identity code. We present extensive face recognition experiments on two challenging Visible-Thermal face datasets. We show that the learned identity code is effective in preserving the identity, thus offering useful insights on interpreting and explaining thermal-to-visible face image translation.

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“…In other cases, ROIs are defined semi-automatically or automatically; however, this is done with the condition that the full-frontal view or a certain image area of the face is given [ 67 ]. Some detection methods exploit the advantages of the visual and thermal modalities, respectively, by combining information extracted from visual and thermal imagery of the same scene [ 68 , 69 , 70 ].…”

Section: Technology Used For Measuring Vital Signsmentioning

confidence: 99%

“…Recent advances in deep convolutional neural networks have helped enable sophisticated facial or human body detection and recognition systems, which prove valuable in surveillance and security systems applications [ 69 ]. Existing state-of-the-art facial recognition systems have demonstrated high-performance accuracy for automatic object detection and identification/recognition tasks [ 71 , 72 , 73 , 74 , 75 ].…”

Section: Technology Used For Measuring Vital Signsmentioning

confidence: 99%

Noncontact Sensing of Contagion

Khanam

Chahl

Chahl³

et al. 2021

J. Imaging

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The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations.

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TR-GAN: thermal to RGB face synthesis with generative adversarial network for cross-modal face recognition

Cited by 20 publications

References 0 publications

Recent Advances in Deep Learning Techniques for Face Recognition

Recent Advances in Deep Learning Techniques for Face Recognition

Explainable Thermal to Visible Face Recognition Using Latent-Guided Generative Adversarial Network

Noncontact Sensing of Contagion

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