2014 · 29 citations · 20 references
ComputingComputer ScienceComputer VisionMachine VisionRemote SensingFace RecognitionImage ClassificationMultispectral ImagingNear-infrared ImagingNear-infrared SpectroscopyPattern RecognitionAutomatic RecognitionBiometric MatchingCross-spectral AnalysisFace VerificationFeature MatchingPartial Face RecognitionPartial RecognitionSpectral Matching
Partial face recognition has been a problem of interest for more than a decade. Most of previous publications on partial face recognition assume intra spectral matching. Matching Short Wave Infrared (SWIR), Middle Wave Infrared (MWIR) or Near Infrared (NIR) images of partial face to a gallery of color images is a much more challenging task. The photometric properties of images in these four spectral bands are highly distinct. Because of the limited space - and also sufficient interest to this biometric - in this paper we present results of cross spectral matching applied to periocular regions. Equipped with a well developed automatic recognition algorithm for heterogeneous face, we demonstrate that the algorithm can be tuned and applied to periocular regions for a positive cross spectral matching of SWIR, MWIR and NIR periocular regions to visible periocular regions at short (1.5 m) and long (50 and 106 m) standoff distances. Our numerical analysis demonstrates the results of the matching. To the best of our knowledge, the performance evaluation presented in this paper is the first of its kind.
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