Eye detection in the Middle-Wave Infrared spectrum: Towards recognition in the dark

Bourlai, Thirimachos; Jafri, Zain

doi:10.1109/wifs.2011.6123120

Cited by 20 publications

(9 citation statements)

References 9 publications

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“…Different classification schemes, such as the AdaBoost algorithm, can be used to train the eye detector. -Template-Based methods [20][21][22][23]: such methods use a generic eye model, typically based on eye shapes, to determine the location of the eye. Then, the eye model is moved across the image of a detected face, computing a correlation score.…”

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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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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“…However, in most operational scenarios (e.g., in military and law enforcement applications) [8], face images may have to be acquired not only under variable illumination conditions and poses, but also under variable spectra. The main advantages of using hyper-spectral images for recognition are that they are suitable for covert applications [9] and can be useful in a nighttime environment [10]. An efficient hyper-spectral eye detection algorithm (benchmark technique) that can perform eye detection at variable bands was proposed by Whitelam et al in [5].…”

Section: Introductionmentioning

confidence: 99%

Pupil detection under lighting and pose variations in the visible and active infrared bands

Bourlai

Whitelam

Kakadiaris

2011

2011 IEEE International Workshop on Information Forensics and Security

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We propose a novel and efficient methodology for the detection of human pupils using face images acquired under controlled and difficult (large pose and illumination changes) conditions in variable spectra (i.e., visible, multi-spectral, and short wave infrared (SWIR)). The methodology is based on template matching, and is composed of an offline and an online mode. During the offline mode, band-dependent eye templates are generated for each eye from the face images of a pre-selected number of subjects. Using the eye templates that are generated in the offline mode, the online pupil detection mode determines the locations of the human eyes and the pupils. A combination of texture-and template-based matching algorithms is used for this purpose. Our method achieved a significantly high detection rate, yielding an average of 96.38% pupil detection accuracy across all datasets used. Based on a comparative analysis on different databases, we concluded that: (i) a single methodological approach can be used to efficiently detect human eyes and pupils of face images (with strong pose and illumination variations) acquired in the visible and hyper-spectral bands, and (ii) the use of texture-based matching and normalized band-specific templates significantly increases detection accuracy. To the best of our knowledge, this is the first time in the open literature that the problem of multi-band pupil detection on face images in the presence of lighting and pose variations, is being investigated using a unified algorithm.

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“…In total, 50 subjects participated in our experiments, and the database has 15 indoor (controlled room-temperature environment) and 15 outdoor full-frontal thermal and visible face images of each subject, resulting in a total of 2250 images. 6 We tested our first FR technique by performing a set of eye localization experiments, which showed that human eyes on frontal still MWIR face images can be detected with promising results. In particular, our template-based eye detection technique achieved the best accuracy when ocular templates (that is, templates of the eye and eyebrow regions) were used.…”

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“…In particular, our template-based eye detection technique achieved the best accuracy when ocular templates (that is, templates of the eye and eyebrow regions) were used. 6 In the main FR experiments, we first established a baseline by comparing a probe dataset of visible face images (queries) against a gallery dataset of visible images. We then compared an indoor dataset of MWIR face images against an either indoor or outdoor dataset.…”

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confidence: 99%

“…As expected, cross-spectral experiments resulted in markedly reduced performance, before and after score level fusion of different methods. 6,7 In general, it appears that MWIR modality holds great promise under reasonable operating conditions. Another benefit of using MWIR is the possibility of obtaining the same recognition rates when face images are acquired in complete darkness.…”

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Mid-wave IR face recognition systems

Bourlai¹

2013

SPIE Newsroom

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Eye detection in the Middle-Wave Infrared spectrum: Towards recognition in the dark

Cited by 20 publications

References 9 publications

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

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

Pupil detection under lighting and pose variations in the visible and active infrared bands

Mid-wave IR face recognition systems

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