Mid-wave IR face recognition systems

Bourlai, Thirimachos

doi:10.1117/2.1201302.004745

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…This can be accomplished by using various types of visible (e.g. with telephoto lenses) and infrared sensors (Near, Short-Wave, Mid-Wave, and Long-Wave IR), under either single-spectral or multi-spectral recognition settings [3,4]. We also know that there are certain settings and configurations that provide increased identification performance during night-time operations, when using passive IR sensors [3], active IR sensors [5] or visible band sensors that operate during day-time and at medium distances [6].…”

Section: Introductionmentioning

confidence: 99%

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On designing practical long range near infrared-based face recognition systems

Bourlai

Mavridis

Narang

2016

Image and Vision Computing

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Section: Introductionmentioning

confidence: 99%

“…Cross-scenario matching experiments are discussed in [4]. There, both commercial as well as academic FR algorithms are used, including LBP [1] and LTP [24].…”

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confidence: 99%

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On designing practical long range near infrared-based face recognition systems

Bourlai

Mavridis

Narang

2016

Image and Vision Computing

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“…In order to deal with the problem of identifying people in both at day and night-time environments, at variable distances and illumination conditions, various types of visible (e.g. with telephoto lenses) and infrared sensors (Near, Short-Wave, Mid-Wave, and Long-Wave IR), under either single-spectral or multi-spectral recognition settings are required [9,10]. We also know that there are certain settings and configurations that provide increased identification performance during night-time operations when using passive IR sensors [9], active IR sensors [11] or visible band sensors that operate during day-time and at medium distances [12].…”

Section: Contributionsmentioning

confidence: 99%

Erbium alloyed AlN thin films: Structural, piezoelectric and magnetic properties

Narang¹

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Deep Features based Hierarchical Classification Scheme for Face Recognition in Heterogeneous Environments Neeru Narang This dissertation investigates the advantages and limitations of the heterogeneous problem of matching infrared long-range, night time face images against their visible counterparts. The contributions of the thesis are threefold. First, a multi-feature scenario dependent fusion scheme is developed, where Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Next, a set of fusion score level schemes are developed and applied before face matching. The developed fusion scheme results in 54 percent point (pp) higher recognition performance than the baseline established using a commercial and a set of academic face matchers. Second, to further improve the performance of baseline face recognition (FR) systems, a scenario dependent and sensor adaptable convolutional neural network (CNN) is developed that groups the data in terms of demographic information, including scenarios (situations) such as indoors or outdoors data, as well as distance and sensor based data. The automated grouping scheme developed is applied before the FR algorithms are used, improving baseline performance from 48% (all vs. all) to 70% (with data grouping). Third, an image quality restoration scheme is designed and developed. This scheme is beneficial to FR systems because the quality of face data captured under challenging conditions is affected by a variety of noise factors (including low illumination conditions, variable standoff distances, and uncooperative subjects). Thus, image quality is responsible for the performance degradation of conventional FR matchers. The developed scheme improves, first, the quality of distorted face images and, then, FR performance in terms of the rank-1 identification rate. Based on the experimental results the major conclusion from this research is that the aforementioned schemes discussed in this dissertation significantly improve cross-spectral face matching performance on diverse scenarios, when used either independently or in combination. The experimental results are further supported by statistical analysis tests, conducted to find the statistical significance of incorporating the developed score level fusion schemes, as well as the demographic filtering to FR systems.

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“…Finally, different facial features can be extracted in the SWIR band that can be combined with those extracted in the visible band to create a more accurate and 157 complete representation of a subjects face [20]. Previous studies [57], discussed in Chapter 2, determined that this capability resulted in an increase of rank-one identification rates under variable face matching scenarios.…”

Section: Introductionmentioning

confidence: 99%

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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Mid-wave IR face recognition systems

Abstract: Biometric data enhances defense and security capabilities for identification of humans at day and night, and in variable weather and environmental conditions.

Cited by 7 publications

References 6 publications

On designing practical long range near infrared-based face recognition systems

On designing practical long range near infrared-based face recognition systems

Erbium alloyed AlN thin films: Structural, piezoelectric and magnetic properties

Eye Detection and Face Recognition Across the Electromagnetic Spectrum

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