Fast Detection of Multi-View Face and Eye Based on Cascaded ...

4) Output the classifier Ksign f k( x) k 1A weak classifier consists <strong>of</strong> a threshold value <strong>and</strong> asimple feature, as shown in Fig. 1. The specific valuesare determined according to an iterative learning. Thesimple features are designed to detect an edge or a line<strong>of</strong> the face easily. Several supplementary features areadded to Viola <strong>and</strong> Jones’ features [10].In the learning stage, all possible positions, sizes, <strong>and</strong>types <strong>of</strong> features are considered within a 2424 window.There are a total number <strong>of</strong> 117,400 features with somerestrictions to their freedom such as minimum area. Aface/non-face strong classifier is applied to the inputimage in all possible positions with all possible sizes inorder to detect all the faces with various positions <strong>and</strong>sizes.However, accordingaswerotatethesimplefeature<strong>of</strong>adetectorwith90°orperform amirroringoperationonthesimplefeature, wegetotherdetectors.Forexample,aleft-view detectorcanbearight-view detector.Also,12 frontal-view detectorscanbemadefrom only2 detectors.Astheresult,thenumber<strong>of</strong>facedetectorstobetrainedisshowninTableI.3.2 The Structure <strong>of</strong> <strong>Multi</strong>-<strong>View</strong> <strong>Face</strong> DetectorInordertodetectmulti-view face, whenweuse12 or72 facedetectorsindependently, weneed12 timesor72times<strong>of</strong>computationcomparingtoasinglefacedetector.Ifweadoptaposeestimator, therehappensaposeestimator’errorproblemaswementioned.Weproposeanew multi-view detectorbased on a specialcascadedclassifier structure using coarse-to-fine search, simple-to-complexsearch, <strong>and</strong> paralel-to-separated searchasshowninFig.2.(a)(b)Fig. 1. Haar-like simple features: (a) edge features;(b)line features.3 <strong>Multi</strong>-<strong>View</strong> <strong>Face</strong> <strong>Detection</strong>3.1 Detectable Rotation AngleFor x-axis rotation, we need only 2 detectors. Theyare a down-view face detector covering [-60, -20]<strong>and</strong>afrontal/upwardfacedetectorcovering[-20, 50].Fory-axisrotation, weneed3 detectorsincludingaleft-viewfacedetector, afrontalfacedetector<strong>and</strong> aright-viewface detector.Theircovering anglesare [-90, -20],[-20, 20], [20, 90], respectively.Forz-axisrotation, wedealwithalrotationcovering[-180, 180].However, duringst<strong>and</strong>ing, apersoncanlean his/her head with [-45, 45].W e cal“Basicmode”<strong>of</strong>z-axisrotationas[-45, 45]<strong>and</strong>“Extendedmode”<strong>of</strong>z-axisrotationas[-180, 180].W hen wedesign adetectorcovering 30°on z-axisrotation, 12 detectorscoverstheextendedmode, [-180°,180°].Forthe basic mode, 3 detectorsare sufficient.Thisiscaledas“MethodI.”W henwedesignadetectorcovers45°, 8detectorscancoverstheextendedmode.Forthebasicmode, 2 detectorsaresuficient.Thisiscaled as“Method II.”W hen weconsiderx, y, z-axisrotation, thenumber<strong>of</strong>facedetectorsneededisshowninTableI.TableI.Thenumbers<strong>of</strong>individualfacedetectorsneededinmulti-view facedetection.MethodIMethodIINumber<strong>of</strong>faceDetectorsneeded.Number<strong>of</strong>facedetectorstobetrainedBasicmode 18=233 10=25Extendedmode72=2312 10=25Basicmode 12=232 6=23Extendedmode48=238 6=23(a) (b) (c)Fig.2.Threemethodsrenderingacascadedclassifierusedin the multi-view face detector;(a)Coarse-to-fine search; (b) Simple-to-complexsearch;(c)Paralel-to-separatedsearch.Coarse-to-finesearchisthatawhole-view classifierislocatedinearlystage<strong>and</strong>narrower-view classifiersarelocated in latestage.Simple-to-complex search isthateasierclassifiersare located in early stage <strong>and</strong> morecomplexclassifiersarelocatedinlatestage.Usingthesetwostages, wecanspeedamulti-view detectorupsincemostnon-facesareeliminatedinearlystages.Paralel-to-separated search isthataldetectorsarearranged in paraleluntilK stage<strong>and</strong> each detectorisarrangedseparatelyfrom K+1stage.Usualy, whenaninputisentered<strong>and</strong>afaceisdetectedsuccessfulyinacertainstage<strong>of</strong>aview, theinputmovestonextstage<strong>of</strong>thesameview tobetested.Paralelarrangementmeansthatwhenafaceisnotdetected, theinputmovestothesamestage<strong>of</strong>nextview.Separatedarrangementmeansthatwhenafaceisnotdetected, nomoreprocedureisneeded.Wejustdecidetheinputisanon-face.Usingthismethod, aninputimageisdecidedasacertainviewinearlystage, <strong>and</strong>thenweconcentrateonwhethertheinputisafaceornotinlatestage.InFig.2, s st<strong>and</strong>sforsuccessinfacedetection<strong>and</strong>f st<strong>and</strong>sforfailure.We combine three cascading methods into amulti-view facedetectorasshowninFig.3.In1~2 stage,whole-view faceisdetected.In3~4stage, facegroupssuchasuprightface, left-leanedface, right-leanedfacearedetected.In5~M stage, alview facearedetected.Also, paralelsearchesare implemented untilK stage<strong>and</strong>separatedsearchesareimplementedfrom K+1stage.InFig.3, NF st<strong>and</strong>sfornon-face, s st<strong>and</strong>sforsuccessinfacedetection, <strong>and</strong>f st<strong>and</strong>sforfailure.117