SWIR imaging for facial image capture through tinted materials

Ice, Jason; Narang, Neeru; Whitelam, Cameron; Kalka, Nathan D.; Hornak, L.A.; Dawson, Jeremy; Bourlai, Thirimachos

doi:10.1117/12.919105

Cited by 10 publications

(14 citation statements)

References 13 publications

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“…For such random on-off patterns we use ρ = p, since for large n (which is our focus) the number of on-elements is ≈ np. Random uniform patterns: We also study patterns consisting of elements that can partially absorb light, e.g., [20,7]. We focus on random such patterns where each entry of a is IID Uniform([0, 1]).…”

Section: Modelmentioning

confidence: 99%

Analysis and Optimization of Aperture Design in Computational Imaging

Yedidia¹,

Thrampoulidis²,

Wornell³

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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There is growing interest in the use of coded aperture imaging systems for a variety of applications. Using an analysis framework based on mutual information, we examine the fundamental limits of such systems-and the associated optimum aperture coding-under simple but meaningful propagation and sensor models. Among other results, we show that when thermal noise dominates, spectrally-flat masks, which have 50% transmissivity, are optimal, but that when shot noise dominates, randomly generated masks with lower transmissivity offer greater performance. We also provide comparisons to classical pinhole cameras.Index Termscoded aperture cameras, computational photography, optical signal processing

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Section: Modelmentioning

confidence: 99%

Analysis and Optimization of Aperture Design in Computational Imaging

Yedidia¹,

Thrampoulidis²,

Wornell³

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The SWIR band has a longer wavelength range than the NIR and is more tolerant to low levels of obscurants like fog and smoke. SWIR band also provides the capability to image through certain types of tinted glass [4]. The passive IR band is further divided into the mid-wave (MWIR) and long-wave (LWIR) infrared bands.…”

Section: Introductionmentioning

confidence: 99%

On designing an unconstrained tri-band pupil detection system for human identification

Whitelam

Bourlai

2015

Machine Vision and Applications

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The majority of facial recognition systems depend on the accurate location of both the left and right eye centers in an effort to geometrically normalize the face images available in a database under study. In this paper, we propose a novel pupil detection algorithmic approach that automatically and efficiently locates the eye centers of face images captured using visible and infrared sensors when operating under challenging conditions. Our proposed approach includes the usage of long-range and night-time face images captured in the infrared (IR) band under active illumination. It also efficiently deals with partial face obstruction (subjects wearing eye glasses) as well as face pose and illumination variation. Our scenario-adaptable methodological approach involves a number of algorithmic steps, including (i) situation classification (to automatically determine the acquisition scenario each input face image is coming from), (ii) generation and usage of 2D normalized correlation coefficients, (iii) prediction of eye localization, (iv) computation of summation range filters for accurate pupil detection, and an (v) eye glasses classifier on facial images using support vector machines (to determine when subjects are wearing glasses or not). Our proposed approach is compared against state-of-the-art academic and commercial eye detection algorithms, including the (a) Viola and Jones Adaboost method, (b) one of the latest academic eye detection algorithms proposed by Valenti and Gevers (IEEE TPAMI), and (c) the eye detection approach that is available as part of the G8 commercial face recognition software B Cameron Whitelam (package provided by L1 systems). Experimental results demonstrate that our proposed approach outperforms all other approaches when applied on various challenging face datasets, including IR face images captured behind tinted glass or at long ranges up to about 350 feet away, day or night. We also show the benefits of our approach to face normalization and, as a result, face recognition improvement performance in terms of rank-1 identification rates. This is an important achievement that has practical value for forensic tool operators who may have to manually localize the eye centers of all face images available in a dataset, before further face image preprocessing and face-based matching algorithms can be applied.

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“…To summarize the findings of these studies, which are published in more detail in [16], temperature did not significantly impact the emission properties of the tinted materials. The largest effect on capturing face images behind the different tinted material was the lighting scenarios, i.e.…”

Section: Tinted Materials Characterizationmentioning

confidence: 92%

“…The SWIR band also provides the capability to image through certain types of tinted glass [16], such as automotive tint and different forms of sunglasses, due to the low absorption of the tinting materials at these wavelengths. The passive IR band, which does not need to be illuminated in order to be images, is further divided into the Mid-wave (MWIR) and…”

Section: Advantages Of the Ir Spectrummentioning

confidence: 99%

“…The booth was designed with a removable front panel in which different tinted pieces of glass with varying transparency could be slid down into. For more details into these materials, please see [16]. Because test images were being captured with interference and obstructing reflections on the glass, the camera setup was slightly angled (∼ 12 • ) to reduce the impact of reflections on images under high exterior lighting conditions at a short distance (2m).…”

Section: Swir Face Image Collectionmentioning

confidence: 99%

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Eye Detection and Face Recognition Across the Electromagnetic Spectrum

Whitelam¹

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Eye Detection and Face Recognition Across the Electromagnetic Spectrum Cameron F. Whitelam Biometrics, or the science of identifying individuals based on their physiological or behavioral traits, has increasingly been used to replace typical identifying markers such as passwords, PIN numbers, passports, etc. Different modalities, such as face, fingerprint, iris, gait, etc. can be used for this purpose. One of the most studied forms of biometrics is face recognition (FR). Due to a number of advantages over typical visible to visible FR, recent trends have been pushing the FR community to perform cross-spectral matching of visible images to face images from higher spectra in the electromagnetic spectrum. In this work, the SWIR band of the EM spectrum is the primary focus. Four main contributions relating to automatic eye detection and cross-spectral FR are discussed. First, a novel eye localization algorithm for the purpose of geometrically normalizing a face across multiple SWIR bands for FR algorithms is introduced. Using a template based scheme and a novel summation range filter, an extensive experimental analysis show that this algorithm is fast, robust, and highly accurate when compared to other available eye detection methods. Also, the eye locations produced by this algorithm provides higher FR results than all other tested approaches. This algorithm is then augmented and updated to quickly and accurately detect eyes in more challenging unconstrained datasets, spanning the EM spectrum. Additionally, a novel cross-spectral matching algorithm is introduced that attempts to bridge the gap between the visible and SWIR spectra. By fusing multiple photometric normalization combinations, the proposed algorithm is not only more efficient than other visible-SWIR matching algorithms, but more accurate in multiple challenging datasets. Finally, a novel pre-processing algorithm is discussed that bridges the gap between document (passport) and live face images. It is shown that the pre-processing scheme proposed, using inpainting and denoising techniques, significantly increases the cross-document face recognition performance. iii This work is dedicated to my family, whom without I would never have been able to complete this work. Their unwavering encouragement, understanding, and advice kept my head looking forward when all I wanted do was turn around. iv Acknowledgments First, and foremost, I would like to give my deepest gratitude to my committee chair and advisor Dr. Thirimachos Bourlai. Through your unique mentoring abilities, I have not only learned the technical and research skills one needs to complete this doctorate program, but have advanced my education on integrity and morality. I will never forget the lessons you have taught me and will strive to exude them in all of my personal and professional relationships. I am truly thankful for everything that you have done for me and will always be indebted to you. ευχαριστώ.

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SWIR imaging for facial image capture through tinted materials

Cited by 10 publications

References 13 publications

Analysis and Optimization of Aperture Design in Computational Imaging

Analysis and Optimization of Aperture Design in Computational Imaging

On designing an unconstrained tri-band pupil detection system for human identification

Eye Detection and Face Recognition Across the Electromagnetic Spectrum

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