alejandro toledo amelie mauresmo angelina jolie atal bihari vajpayee carlos menem david beckham george bush gerhard schroeder gray davis hamid karzai hugo chavez laura bush lleyton hewitt mahmoud abbas michael bloomberg nestor kirchner pete sampras ricardo lagos rudolph giuliani serena williams tiger woods tom ridge vicente fox winona ryder alvaro uribe andre agassi arnold schwarzenegger bill clinton colin powell donald rumsfeld george robertson gloria macapagal arroyo guillermo coria hans blix igor ivanov lindsay davenport luiz inacio lula da silva megawati sukarnoputri naomi watts paul bremer recep tayyip erdogan roh moo-hyun saddam hussein silvio berlusconi tom daschle tony blair vladimir putin to make the data less skewedwe will only take up to images of each person (otherwisethe feature extraction would be overwhelmed by the likelihood of george bush)in[ ]mask np zeros(people target shapedtype=np boolfor target in np unique(people target)mask[np where(people target =target)[ ][: ] x_people people data[masky_people people target[maskscale the grayscale values to be between and instead of and for better numeric stability x_people x_people common task in face recognition is to ask if previously unseen face belongs to known person from database this has applications in photo collectionsocial mediaand security applications one way to solve this problem would be to build classifier where each person is separate class howeverthere are usually many different people in face databasesand very few images of the same person ( very few training examples per classthat makes it hard to train most classifiers additionallydimensionality reductionfeature extractionand manifold learning