Collection of Multispectral Biometric Data for Cross-spectral Identification Applications

Dawson, Jeremy; Leffel, Seth C.; Whitelam, Cameron; Bourlai, Thirimachos

doi:10.1007/978-3-319-28501-6_2

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…In some situations, like surveillance and border control, it may happen that the NIR images captured in the enrolment phase with controlled conditions should be compared against images captured under VIS illumination in a less constrained environment. The cross-spectral comparison is gaining interest for ocular traits and others like face [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Sequeira

Chen

Ferryman

et al. 2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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This work presents the 2 nd Cross-Spectrum Iris/Periocular Recognition Competition (CrossEyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward.

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Section: Introductionmentioning

confidence: 99%

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Sequeira

Chen

Ferryman

et al. 2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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“…Far-infrared (FIR) cameras can partially overcome the above-mentioned limitations of RGB cameras [11]. FIR cameras detect the thermal radiation emitted from an object in the 3-100 µm band, hence the image quality does not deteriorate with illumination changes.…”

Section: Introductionmentioning

confidence: 99%

Improving ROI detection in photoplethysmographic imaging with thermal cameras

Scebba

Dragas

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Photoplethismographic imaging (PPGi) enables the estimation of heart rate without body contact by analyzing the temporal skin color changes from video recordings. Motion artifacts and atypical facial characteristics cause poor signals and currently limit the applicability of PPGi. We have developed a novel algorithm for locating cheek and forehead region of interests (ROI) with the aim to improve PPGi during challenging situations. The proposed approach is based on the fusion of RGB and far-infrared (FIR) video streams where FIR ROI is used as fall-back when RGB alone fails. We validated and compared the algorithm against the detection based on single sources, using videos from 8 subjects with distinctively different face characteristics. The subject performed three scenarios with incremental motion artifact content (head at rest, intensive head movements, speaking). The results showed that combining the two imaging sources increased the detection rate of cheeks from 75% (RGB) to 92% (RGB+FIR) in the challenging intensive head movement scenario. This work demonstrated that FIR imaging is complementary to simple RGB imaging and when combined, adds robustness to the detection of ROI in PPGi applications.

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“…In this work, WVU-MS database [61,72] is collected in our lab in the SWIR band. The database was captured using Goodrich SU640 (an Indium Gallium Arsenide) video camera featuring high sensitivity in the spectral range from 900-1700 nm and wide dynamic range.…”

Section: Multi-scenario Swir Databasementioning

confidence: 99%

“…• One additional, challenge was to automatically locate the eye locations to normalize the face i.e. for multi-wavelength database when the face images are collected at 1450 nm wavelength, through tinted glass panels (Solarcool Gray-lite Glass Panel) [61,72] in SWIR band and night time face images are collected outdoors at long standoff distances of 30m to 120m. To address this problem, the eye locations were located manually.…”

Section: Future Workmentioning

confidence: 99%

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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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Collection of Multispectral Biometric Data for Cross-spectral Identification Applications

Cited by 7 publications

References 25 publications

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Improving ROI detection in photoplethysmographic imaging with thermal cameras

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

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