Guest editorial: Special issue on computer vision beyond the visible spectrum

Abstract

It was three years ago that we had our first special issue inComputer Vision Beyond the Visibel Spectrum (CVBVS) inthe Machine Vision and Applications journal [1]. At thattime, we expected that computer vision research wouldgrow significantly in the nonvisible spectrum, particularly inthe biometrics area. This second special issue in the Imageand Vision Computing journal comes to verify ourexpectations. New CVBVS methods and systems in facedetection, tracking, and recognition enable identification ata distance under any lighting conditions. As a result, thisnew technology heralds the migration of human identifi-cation screening from access control to surveillanceapplications. These adavances promise to revolutionizethe security industry. Also, methods and systems for defenseapplications, which is the traditional CVBVS domain, grewmore sophisticated and effective. Target detection andrecognition in Synthetic Aperture Radar (SAR) imagery isat the very core of these advancements.Allthepapersincludedinthisspecialissuewerepresentedin preliminary form at the IEEE Workshop in ComputerVision Beyond the Visible Spectrum, held in Kauai, Hawaii,on December 14, 2001. The selected papers were a smallsubset of the total number of papers presented at theWorkshop and underwent two rounds of further rigorousreview and updates to ensure a high quality outcome.In the first paper Face Detection in the Near-IRSpectrum, J. Dowdall et al. introduce a new face detectionmethod based on the fusion of two near-infrared bands. Theresulting phenomenology is such that it allows reliable facedetection with simple algorithmic means like integralprojection. The authors realized a prototype system basedon their method and performed comparative experimentaltests with a state-of-the-art visible spectrum face detector.The near-infrared detector exhibited superior performanceat various ambient illumination conditions and in thepresence of extreme face rotation. This face detectionsystem in combination with a face recognizer is employedon a trial basis for gate control in a practical application.In the second paper Tracking Human Faces in InfraredVideo, C.K. Eveland et al. describe a novel face detectionand tracking system in thermal infrared. In contrast to thenear-infrared face detector proposed by J. Dowdall et al. inthe first paper, this system is totally passive. The underlyingmethod has two components: first, a face segmentationscheme that is based on a thermal emission model from thehuman skin; and second, a condensation algorithm that usesthe segmentation model for tracking the face over time. Themethod is characterized by mathematical elegance and issupported by promising experimental results.The third paper is on Statistical Hypothesis Pruning forRecognizing Faces from Infrared Images by A. Srivastava.This paper complements the face detection and trackingmethods presented in the first two papers. Recognition isaccomplished by hypothesis pruning using spectraldecomposition of the observed images and their lower-order statistics. This is another elegant method that issupported by comparative experimental results. The authorsused the data sets released by the first two papers, whichreinforces the complimentary nature of the approachestowards an infrared identification system.The fourth paper Genetic Algorithm Based FeatureSelection for Target Detection in SAR Images, by B. Bhanuand Y. Lin introduces a Genetic Algorithm (GA) approachfor target feature selection in SAR imagery. The GAalgorithm is driven by a new fitness function based on