Deep Cross Polarimetric Thermal-to-Visible Face Recognition

Iranmanesh, Seyed Mehdi; Dabouei, Ali; Kazemi, Hadi; Nasrabadi, Nasser M.

doi:10.1109/icb2018.2018.00034

Cited by 40 publications

(26 citation statements)

References 38 publications

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“…Recently, there has been a growing number of approaches that bridge existing modality gaps in order to perform heterogeneous face recognition. These approaches focused on various scenarios of heterogeneous face recognition such as infrared-to-visible [30,16,56], thermal-to-visible [51,48,76,21,27], and sketch-to-visible [10,39] [60]. Fundamentally, each approach seeks to either find a common latent subspace in which corresponding faces from each domain are "close" in terms of some distance and non-corresponding faces are "far."…”

Section: Heterogeneous Face Recognitionmentioning

confidence: 99%

Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks

Riggan

et al. 2019

Int J Comput Vis

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The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domain makes cross-domain face verification a highly challenging problem for human examiners as well as computer vision algorithms. Previous approaches utilize either a twostep procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multichannel input to synthesize the visible image given the corresponding polarimetric signatures. Although these methods have yielded improvements, we argue that input-level fusion alone may not be sufficient to realize the full potential of the available Stokes images. We propose a Generative Adversarial Networks (GAN) based multi-stream featurelevel fusion technique to synthesize high-quality visible images from prolarimetric thermal images. The proposed network consists of a generator sub-network, constructed using an encoder-decoder network based on dense residual

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Section: Heterogeneous Face Recognitionmentioning

confidence: 99%

Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks

Riggan

et al. 2019

Int J Comput Vis

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“…Second, the generator should preserve the identification information embedded in minutiae and ridge patterns. To extract and preserve the identification information we developed a method that is inspired by perceptual loss [4,7,12,17] and multi-level feature abstraction [27,28,13,10]. For this purpose, we separately trained a deep Siamese CNN as a fingerprint verifier.…”

Section: Cgan For Latent Fingerprint Reconstructionmentioning

confidence: 99%

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

Dabouei

Soleymani

Kazemi

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latent fingerprint reconstruction model based on conditional generative adversarial networks (cGANs). Two modifications are applied to the cGAN to adapt it for the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information are considered in the generation process, and prevent the model from filling large missing areas and generating erroneous minutiae. Second, a perceptual ID preservation approach is developed to force the generator to preserve the ID information during the reconstruction process. Using a synthetically generated database of latent fingerprints, the deep network learns to predict missing information from the input latent samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms on several publicly available latent fingerprint datasets. We achieved rank-10 accuracy of 88.02% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50 accuracy of 70.89% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching. Experimental results of matching reconstructed samples in both latent-to-sensor and latent-to-latent frameworks indicate that the proposed method significantly increases the matching accuracy of the fingerprint recognition systems for the latent samples.

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“…In addition, Riggan et al [25] proposed a combination of PLS classifier with two different feature mapping approaches: Coupled Neural Network CpNN and Deep Perceptual Mapping (DPM) to utilize the features derived from the Stokes images for cross-modal face recognition. Recently, Iranmanesh et al [11] proposed a two stream Deep Convolutional Neural Networks (DC-NNs) (Vis-DCNN and Pol-DCNN) to learn a discriminative metric for this cross-domain verification. Some of the other visible to thermal cross-modal matching methods include [7,29].…”

Section: Introductionmentioning

confidence: 99%

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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Deep Cross Polarimetric Thermal-to-Visible Face Recognition

Cited by 40 publications

References 38 publications

Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks

Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

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

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