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

Bourlai, Thirimachos; Kalka, Nathan D.; Ross, Arun; Čukić, Bojan; Hornak, L.A.

doi:10.1109/icpr.2010.1115

Cited by 65 publications

(46 citation statements)

References 12 publications

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“…Some works [1], [7] utilized the same methodology as in visible face detection, while many other works [5], [6] simply omitted it. Currently, Boosting [20] based Viola-Jones framework [11] dominates the field of face detection, and numerous Manuscript variants in either framework or feature types, such as Nesting [8], Vector Boosting [9], MBLBP [10], have been proposed.…”

Section: Introductionmentioning

confidence: 97%

Regularized Transfer Boosting for Face Detection Across Spectrum

Zhang

Dong

Lei

et al. 2012

IEEE Signal Process. Lett.

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This letter addresses the problem of face detection in multispectral illuminations. Face detection in visible images has been well addressed based on the large scale training samples. For the recently emerging multispectral face biometrics, however, the face data is scarce and expensive to collect, and it is usually short of face samples to train an accurate face detector. In this letter, we propose to tackle the issue of multispectral face detection by combining existing large scale visible face images and a few multispectral face images. We cast the problem of face detection across spectrum into the transfer learning framework and try to learn the robust multispectral face detector by exploring relevant knowledge from visible data domain. Specifically, a novel Regularized Transfer Boosting algorithm named R-TrBoost is proposed, with features of weighted loss objective and manifold regularization. Experiments are performed with face images of two spectrums, 850 nm and 365 nm, and the results show significant improvement on multispectral face detection using the proposed algorithm.

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Section: Introductionmentioning

confidence: 97%

Regularized Transfer Boosting for Face Detection Across Spectrum

Zhang

Dong

Lei

et al. 2012

IEEE Signal Process. Lett.

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“…In order to address these issues recent research has moved into the use of infrared imagery (e.g., intensified nearinfrared (NIR) [1], Short Wave IR [2], Long Wave IR [3]). Table I summarizes the electromagnetic bands of interest, their wavelength range, and illumination sources.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Eye Detection: A Preliminary Study

Whitelam

Jafri

Bourlai

2010

2010 20th International Conference on Pattern Recognition

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In this paper the problem of eye detection across three different bands, i.e., the visible, multispectral, and short wave infrared (SWIR), is studied in order to illustrate the advantages and limitations of multi-band eye localization. The contributions of this work are two-fold. First, a multi-band database of 30 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 for multi-band eye detection. Experiments show that the eyes on face images captured under different bands can be detected with promising results. Finally, we illustrate that recognition performance in all studied bands is favorably affected by the geometric normalization of raw face images that is based on our proposed detection methodology. To the best of our knowledge this is the first time that this problem is being investigated in the open literature in the context of human eye localization across different bands.

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“…Further, NIR images are less susceptible to changes in ambient illumination. Thus, cross-spectral matching has become an important topic of research [8,12,2]. To the best of our knowledge, this is the first work that explores gender recognition in NIR face images.…”

Section: Introductionmentioning

confidence: 99%

“…CLAHE is effective at improving local contrast without inducing much noise. It utilizes the normalized cumulative distribution of each gray level, x, in the block [2]:…”

Section: Clahe Normalizationmentioning

confidence: 99%

Can Gender Be Predicted from Near-Infrared Face Images?

Ross

Chen

2011

Lecture Notes in Computer Science

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Abstract.Gender classification based on facial images has received increased attention in the computer vision literature. Previous work on this topic has focused on images acquired in the visible spectrum (VIS). We explore the possibility of predicting gender from face images acquired in the near-infrared spectrum (NIR). In this regard, we address the following two questions: (a) Can gender be predicted from NIR face images; and (b) Can a gender predictor learned using VIS images operate successfully on NIR images and vice-versa? Our experimental results suggest that NIR face images do have some discriminatory information pertaining to gender, although the degree of discrimination is noticeably lower than that of VIS images. Further, the use of an illumination normalization routine may be essential for facilitating cross-spectral gender prediction.

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

Cited by 65 publications

References 12 publications

Regularized Transfer Boosting for Face Detection Across Spectrum

Regularized Transfer Boosting for Face Detection Across Spectrum

Multispectral Eye Detection: A Preliminary Study

Can Gender Be Predicted from Near-Infrared Face Images?

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