Ascertaining Human Identity in Night Environments

Bourlai, Thirimachos; Kalka, Nathan D.; Cao, Deng; DeCann, Brian; Jafri, Zain; Nicolò, Francesco; Whitelam, Cameron; Zuo, Jinyu; Adjeroh, Donald A.; Čukić, Bojan; Dawson, Jeremy; Hornak, L.A.; Ross, Arun; Schmid, Natalia A.

doi:10.1007/978-0-85729-127-1_30

Cited by 22 publications

(9 citation statements)

References 6 publications

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“…For example, face recognition in nearinfrared (NIR) [17,32] and thermal (THM) [26] spectra has been motivated by the need to determine human identity in nighttime environments [7]. Furthermore, changes in ambient illumination have lesser impact on face images acquired in these spectra than the visible spectrum.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of gender classification methods on thermal and near-infrared face images

Chen

Ross

2011

2011 International Joint Conference on Biometrics (IJCB)

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Automatic gender classification based on face images is receiving increased attention in the biometrics community. Most gender classification systems have been evaluated only on face images captured in the visible spectrum. In this work, the possibility of deducing gender from face images obtained in the near-infrared (NIR) and thermal (THM) spectra is established. It is observed that the use of local binary pattern histogram (LBPH) features along with discriminative classifiers results in reasonable gender classification accuracy in both the NIR and THM spectra.Further, the performance of human subjects in classifying thermal face images is studied. Experiments suggest that machine-learning methods are better suited than humans for gender classification from face images in the thermal spectrum.

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Section: Introductionmentioning

confidence: 99%

Evaluation of gender classification methods on thermal and near-infrared face images

Chen

Ross

2011

2011 International Joint Conference on Biometrics (IJCB)

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“…However, in harsh environmental conditions characterized by unfavorable lighting and pronounced shadows (such as a nighttime environment [1]), human recognition based only on visible spectral images may not be feasible [2]. Thus, recognition of faces in the infrared spectrum has become an area of growing interest [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Cross-Spectral Face Verification in the Short Wave Infrared (SWIR) Band

Bourlai

Kalka

Ross

et al. 2010

2010 20th International Conference on Pattern Recognition

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Abstract-The problem of face verification across the short wave infrared spectrum (SWIR) is studied in order to illustrate the advantages and limitations of SWIR face verification. The contributions of this work are two-fold. First, a database of 50 subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is performed in order to demonstrate the possibility of SWIR cross-spectral matching. Experiments also show that images captured under different SWIR wavelengths can be matched to visible images with promising results. The role of multispectral fusion in improving recognition performance in SWIR images is finally illustrated. To the best of our knowledge, this is the first time cross-spectral SWIR face recognition is being investigated in the open literature.

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“…However, in real-world situations (military and law enforcement), we have to deal with harsh environmental conditions, characterized by difficult lighting, which can cause unfavorable shadows and thus limit the effectiveness of conventional FR systems in terms of their human identification accuracy. For example, in low-light environments, human recognition, based solely on visible band face images, may not be feasible or accurate enough [1][2][3]. In order to deal with such difficult environments, camera sensors operating Fig.…”

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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Ascertaining Human Identity in Night Environments

Cited by 22 publications

References 6 publications

Evaluation of gender classification methods on thermal and near-infrared face images

Evaluation of gender classification methods on thermal and near-infrared face images

Cross-Spectral Face Verification in the Short Wave Infrared (SWIR) Band

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

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