A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums

Espinosa-Duro, V.; Faundez-Zanuy, Marcos; Mekyska, Jiří

doi:10.1007/s12559-012-9163-2

Cited by 79 publications

(45 citation statements)

References 23 publications

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“…Similarly, Maeng et al [12] utilized HOG features for cross-spectrum and cross-distance face matching. Most of these algorithms are evaluated on small scale datasets, such as heterogeneous face biometrics (HFB) dataset [13] and CARL [14] which comprise limited number of subjects and/or undefined experimental protocols. Therefore, claims regarding generalizability of performance may not be made confidently and benchmarking can be challenging.…”

Section: A Literature Reviewmentioning

confidence: 99%

On Effectiveness of Histogram of Oriented Gradient Features for Visible to Near Infrared Face Matching

Dhamecha

Sharma

Singh

et al. 2014

2014 22nd International Conference on Pattern Recognition

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The advent of near infrared imagery and it's applications in face recognition has instigated research in cross spectral (visible to near infrared) matching. Existing research has focused on extracting textural features including variants of histogram of oriented gradients. This paper focuses on studying the effectiveness of these features for cross spectral face recognition. On NIR-VIS-2.0 cross spectral face database, three HOG variants are analyzed along with dimensionality reduction approaches and linear discriminant analysis. The results demonstrate that DSIFT with subspace LDA outperforms a commercial matcher and other HOG variants by at least 15%. We also observe that histogram of oriented gradient features are able to encode similar facial features across spectrums.

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Section: A Literature Reviewmentioning

confidence: 99%

On Effectiveness of Histogram of Oriented Gradient Features for Visible to Near Infrared Face Matching

Dhamecha

Sharma

Singh

et al. 2014

2014 22nd International Conference on Pattern Recognition

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“…The method was researched using thermal facial replicas from the Terravic Facial Infrared Database [9] and Carl's database [10]. These were processed using the Large Time-Frequency Analysis Toolbox (LTFAT) in Octave [57] for FWT and Byte fish face recognition toolbox [58] was used for facial image set importation tools.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…From these samples, for training and testing the anti-spoof algorithm to achieve reduced vulnerability, were derived. The Object Tracking and Classification Beyond the Visible Spectrum (OTCBVS) benchmark dataset collection [9], and Carl's databases [10], have provided a dataset of images, captured under diverse conditions, using thermal imaging. Which can help research work in the area of person recognition.…”

Section: Data Setsmentioning

confidence: 99%

Multi Biometric Thermal Face Recognition Using FWT and LDA Feature Extraction Methods with RBM DBN and FFNN Classifier Algorithms

Hurwitz¹,

Orji²

2019

Adv. sci. technol. eng. syst. j.

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Person recognition using thermal imaging, multi-biometric traits, with groups of feature filters and classifiers, is the subject of this paper. These were used to tackle the problems of biometric systems, such as a change in illumination and spoof attacks. Using a combination of, hard and soft-biometric, attributes in thermal facial images. The hardbiometric trait, of the shape of a head, was combined with soft-biometric traits such as the face wearing glasses, face wearing a cap/headgear, face with facial hairs, plain face, female face, and male face. These were experimented with, using images from Carl's database and Terravic Facial Infrared Database, and used to train clusters of neural network algorithms for each biometric trait. These comprised Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), and Feed Forward Neural Networks (FFNN). After feature extraction, using Fast Wavelet Transform (FWT), and Linear Discriminant Analysis (LDA). A classification error of 0

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“…Even though there exist several conventional thermal-visible pair databases [9,6], they lack the availability of the corresponding polarization state information such as S 1 and S 2 . Hence, an extended database, which contains polarimetric (S 0 , S 1 , S 2 ) and visible image pairs from 111 subjects is used for evaluation in this paper.…”

Section: Experimental Protocolsmentioning

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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A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums

Cited by 79 publications

References 23 publications

On Effectiveness of Histogram of Oriented Gradient Features for Visible to Near Infrared Face Matching

On Effectiveness of Histogram of Oriented Gradient Features for Visible to Near Infrared Face Matching

Multi Biometric Thermal Face Recognition Using FWT and LDA Feature Extraction Methods with RBM DBN and FFNN Classifier Algorithms

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

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