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《基于GABOR小波的人臉識(shí)別的單樣本問(wèn)題研究》由會(huì)員上傳分享，免費(fèi)在線閱讀，更多相關(guān)內(nèi)容在學(xué)術(shù)論文-天天文庫(kù)。
1、上海交通大學(xué)碩士學(xué)位論文基于Gabor小波的人臉識(shí)別的單樣本問(wèn)題研究姓名：姚榮華申請(qǐng)學(xué)位級(jí)別：碩士專(zhuān)業(yè)：計(jì)算機(jī)軟件與理論指導(dǎo)教師：盧宏濤20070101上海交通大學(xué)碩士學(xué)位論文AbstractFacerecognitionisanimportantsubjectinartificialintelligencefield,andithasgainedextensiveattentionfromresearchersinthepastdecades.Duetothegoodcharacteristicofhumanfaceimage’s
2、Gaborwaveletfeature,facerecognitiontechnologybasedonGaborwaveletisaverypopularmethod.Facerecognitionisanintersectingsubject,andthispaperisfocusingonfeatureextractionoffacerecognition,andalsothispaperproposesanewsolutionforsinglesampleprobleminfacerecognition.Thispaperu
3、tilizesthepropertythatacomplexnumbercanberesolvedintomagnitudeandargumentinapolarcoordinatesystem,extractsargumentfeaturesfromhumanfaceimage’sGaborwaveletrepresentation,andgivesthedistributionfigureofhumanfaceimage’sGaborwaveletrepresentationcontainingargumentfeaturesa
4、ndmagnitudefeatures.Forthesinglesampleproblem,bymakinguseofmagnitudefeaturesandnewextractedargumentfeatures,thispaperproposesanovelEnrichedGaborfeaturebasedPrincipalComponentAnalysis(EGPCA)algorithm.BasedontheEGPCAalgorithm,thispaperimplementsafacerecognitionsystem,and
5、comparesEGPCAwith22(,PC)AE(PC)A,andSVDPerturbationinafacerecognitiontask.ExperimentalresultsonFERETfacedatabaseshowthatEGPCAcanachieve89.5%recognitionratewhenonlyonetrainingimageperpersonisavailableinfacerecognition,whichissuperiortootherthreealgorithms.Forthepurposeof
6、comparingtheefficiencyofargumentfeaturesandphasefeaturesinfacerecognition,thisthesisappliesthesetwokindsoffeaturesinfacerecognitionexperimentsbasedonFERETandORLfacedatabases.Experimentalresultsonbothtwofacedatabasesshowthatargumentfeaturesaresuperiortophasefeatures.Key
7、Words:FaceRecognition,GaborWavelet,FeatureExtraction,PrincipalComponentAnalysis,SingleSampleII上海交通大學(xué)碩士學(xué)位論文符號(hào)說(shuō)明PCA:PrincipalComponentAnalysis主成分分析LDA:LinearDiscriminantAnalysis線性判別分析ICA:IndependentComponentAnalysis獨(dú)立成分分析GA:GeneticAlgorithm遺傳算法EFM:EnhancedFisherlineardis
8、criminantModels增強(qiáng)的Fisher線性判別模型FFT:FastFourierTransform快速傅立葉變換MSE:MeanSquareError最小化均方誤差2(PC)A:Projection-CombinedPrin
