11/08 Notes =========== Face Recognition ---------------- Final Project is on Face Recognition What DPI is best * Algorithm takes longer the higher the DPI * Higher DPI can detect more differences * A balence is required, depends on what you are doing Level 1 * General Shape * Skin Tone Level 2 * Facial Components * eyes/nose/mouth * Relationship between the Components is revealed * have 30 to 75 IPD * *IPD* is Inter Pupilary Distance - Distance between centers of the 2 eyes * the more features you expose, the more vulnerable the image is to intra class variations * Sad vs happey is an example * Low resolution images are affected by this less than high resolution Most use Level 2 Level 3 * Micro Features, which are vulnerable to intra class variarions as well * Scars, freckles, skin discoloration, etc... Face Detection -------------- Uses Adaboost * set of *weak* classifiers that are combined Input Patch (x) --> Classifier (h) --> Face(y=1) or Not Face(y=0) Combine Weak Classifiers ________________________ Classifier = small red window Called Cascade Classifiers * h_1(x) = 0 then non face * h_1(x) = 1 then go to h_2(x) * etc.. h_i( input patch, haar filter, polarity, theta) polarity can be 1 or -1 this should output 1 or 0 1 if filter(x) > p x theta 0 if filter(x) <= p x theta 160,000 filters/classifiers to use Training -------- Algorithm is in the book goal of Training * Select T Classifiers * given set of examples - face or not face * Assign a weight to each of the examplars * Classifiy the examplars using each of the 160,000 classifiers * select the Classifier that gives the *lowest* *weighted* error rate * revise the weight of the examplars as follows #. correctly classified examplars - no weight change #. incorrectly classified examples - increase weight * Loop back to Classifiy the examplars using now 159,999 classifiers * continue till it covers everything? or hits T selected classifiers SHOULD GO OVER INTEGRAL IMAGES * sum of pixels above and to the left of a chosen pixel