LAMP-HQ: A Large-Scale Multi-pose High-Quality Database and Benchmark for NIR-VIS Face Recognition

Yu, Aijing; Wu, Haoxue; Huang, Huaibo; Lei, Zhen; He, Ran

doi:10.1007/s11263-021-01432-4

Cited by 31 publications

(12 citation statements)

References 58 publications

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“…The recognition systems presented in the literature that are based on Gabor wavelet Transform and Support Machine Vector (SVM) achieve a recognition rate of 96.4% [12]. The recognition systems based on Convolutional Neural Network [14], [15], [16], [18], [22] achieve respectively accuracy in [98.6% -99.6%], [94.94% -97.91%] and [98.15%, 95.2%,95.5%], [99.89%, 99.56%],[98.5%-98.8%, 98.5%-99.3%] depending on database. Also [23] used hyperspectrals camera with neural networks and get [99.76%, 100%, 99.6%] accuracies on three different databases.…”

Section: Discussionmentioning

confidence: 99%

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Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

HOUSSOU¹,

Mahama²,

Gouton³

et al. 2022

IJACSA

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Face recognition in the visible and Near Infrared range has received a lot of attention in recent years. The current Multispectral (MS) imaging systems used for facial recognition are based on multiple cameras having multiple sensors. These acquisition systems are normally slow because they take one MS image in several shots, which makes them unable to acquire images in real time and to capture moving scenes. On the other hand, currently there are snapshot multispectral imaging systems which integrate a single sensor with Multispectral Filter Arrays (MSFA) allow having at each acquisition an image on several spectra. These systems drastically reduce image acquisition time and are able to capture moving scenes in real time. This paper proposes a study of robust facial recognition using Multispectral Filter Array acquisition system. For this goal, a MSFA one-shot camera was used to collect the images and a robust facial recognition method based on Fast Discrete Curvelet Transform and Convolutional Neural Network is proposed. This camera covers the spectral range from 650 nm to 950 nm. A comparison of the facial recognition system using Multispectral Filter Arrays camera is made with those that using multiple cameras. Experimental results proved that face recognition systems whose acquisition systems are designed using MSFA perform more efficiently with an accuracy of 100%.

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Section: Discussionmentioning

confidence: 99%

“…A. Yu et al [15] have implemented face recognition system that used Generative Adversarial Networks(GANs) in the VIS and NIR. The VIS images have been generated from the NIR images.…”

Section: Related Workmentioning

confidence: 99%

Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

HOUSSOU¹,

Mahama²,

Gouton³

et al. 2022

IJACSA

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“…Existing DCNNbased visible face recognition methods will not perform well when directly applied to the problem of thermal to visible face recognition due to the significant distributional shift between the thermal and visible domains. In order to bridge this gap, various cross-domain face recognition algorithms have been proposed [7], [14], [13], [43], [44], [47], [29], [27], [5], [37], [36]. In particular, synthesis-based methods have gained a lot of traction in recent years [7].…”

Section: Introductionmentioning

confidence: 99%

“…Wei et al [40] proposed the FFWM model to overcome the illumination issue via flow-based feature warping. Thermal-to-visible Synthesis: Various approaches have been proposed in the literature for thermal-to-visible face synthesis and matching [7], [14], [13], [43], [44], [47], [29], [27], [5], [37], [36], [6], [34], [18]. For instance, Hu et al [14] developed a model based on partial least squares for heterogeneous face recognition.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Face Frontalization via Domain Agnostic Learning

Di,

Hu,

Patel

2021

Preprint

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Recent advances in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on thermal to visible face synthesis and matching problems. However, current DCNN-based synthesis models do not perform well on thermal faces with large pose variations. In order to deal with this problem, heterogeneous face frontalization methods are needed in which a model takes a thermal profile face image and generates a frontal visible face. This is an extremely difficult problem due to the large domain as well as large pose discrepancies between the two modalities. Despite its applications in biometrics and surveillance, this problem is relatively unexplored in the literature. We propose a domain agnostic learning-based generative adversarial network (DAL-GAN) which can synthesize frontal views in the visible domain from thermal faces with pose variations. DAL-GAN consists of a generator with an auxiliary classifier and two discriminators which capture both local and global texture discriminations for better synthesis. A contrastive constraint is enforced in the latent space of the generator with the help of a dual-path training strategy, which improves the feature vector's discrimination. Finally, a multi-purpose loss function is utilized to guide the network in synthesizing identitypreserving cross-domain frontalization. Extensive experimental results demonstrate that DAL-GAN can generate better quality frontal views compared to the other baseline methods.

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“…(iii) Large variation on facial attributes. Face images have various facial attributes including pose, complexion, expression, and illumination, which further increase intra-class distance and make it difficult for face matching [10].…”

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confidence: 99%

Heterogeneous Face Recognition via Face Synthesis With Identity-Attribute Disentanglement

Yang

et al. 2022

IEEE Trans.Inform.Forensic Secur.

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Heterogeneous Face Recognition (HFR) aims to match faces across different domains (e.g., visible to near-infrared images), which has been widely applied in authentication and forensics scenarios. However, HFR is a challenging problem because of the large cross-domain discrepancy, limited heterogeneous data pairs, and large variation of facial attributes.To address these challenges, we propose a new HFR method from the perspective of heterogeneous data augmentation, named Face Synthesis with Identity-Attribute Disentanglement (FSIAD). Firstly, the identity-attribute disentanglement (IAD) decouples face images into identity-related representations and identityunrelated representations (called attributes), and then decreases the correlation between identities and attributes. Secondly, we devise a face synthesis module (FSM) to generate a large number of images with stochastic combinations of disentangled identities and attributes for enriching the attribute diversity of synthetic images. Both the original images and the synthetic ones are utilized to train the HFR network for tackling the challenges and improving the performance of HFR. Extensive experiments on five HFR databases validate that FSIAD obtains superior performance than previous HFR approaches. Particularly, FSIAD obtains 4.8% improvement over state of the art in terms of VR@FAR=0.01% on LAMP-HQ, the largest HFR database so far.

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LAMP-HQ: A Large-Scale Multi-pose High-Quality Database and Benchmark for NIR-VIS Face Recognition

Cited by 31 publications

References 58 publications

Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

Heterogeneous Face Frontalization via Domain Agnostic Learning

Heterogeneous Face Recognition via Face Synthesis With Identity-Attribute Disentanglement

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