ACP based face detection Ramin Marx 1 Mai 2007 1 with support from - PowerPoint PPT Presentation

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software ACP based face detection Ramin Marx 1 Mai 2007 1 with support from Jean-Marc Bo¨ ı and Bernard Fertil Ramin Marx ACP based face detection

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software Introduction Situation many domains deal with human faces (video surveillance, identification) Ramin Marx ACP based face detection

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software Introduction Situation many domains deal with human faces (video surveillance, identification) Problems humans in the picture? or not? where? Ramin Marx ACP based face detection

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software Introduction Situation many domains deal with human faces (video surveillance, identification) Problems humans in the picture? or not? where? Goal algorithm, which locates faces in a given picture Ramin Marx ACP based face detection

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software Input picture Ramin Marx ACP based face detection

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software Output picture Ramin Marx ACP based face detection

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software Approach ◮ calculate the faceness of a region R Ramin Marx ACP based face detection

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software Approach ◮ calculate the faceness of a region R ◮ analyze a training database with a huge number of faces Ramin Marx ACP based face detection

Introduction Analysis Experiments Questions and conclusions Face Detection Program Used Papers/Software Approach ◮ calculate the faceness of a region R ◮ analyze a training database with a huge number of faces ◮ extract the most characteristic features and find out how many of those features contains R Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Idea ◮ treat each of the face images (size n × m ) as vector � v ∈ R n · m Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Idea ◮ treat each of the face images (size n × m ) as vector � v ∈ R n · m ◮ find relationships between dimensions by calculating the covariances of the training faces Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Idea ◮ treat each of the face images (size n × m ) as vector � v ∈ R n · m ◮ find relationships between dimensions by calculating the covariances of the training faces ◮ calculate the eigenvectors of that covariance matrix and sort them descending according to their eigenvalues Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Idea ◮ treat each of the face images (size n × m ) as vector � v ∈ R n · m ◮ find relationships between dimensions by calculating the covariances of the training faces ◮ calculate the eigenvectors of that covariance matrix and sort them descending according to their eigenvalues ◮ new base, in which the i -th base vector contains i -th most information about the data set Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Idea ◮ treat each of the face images (size n × m ) as vector � v ∈ R n · m ◮ find relationships between dimensions by calculating the covariances of the training faces ◮ calculate the eigenvectors of that covariance matrix and sort them descending according to their eigenvalues ◮ new base, in which the i -th base vector contains i -th most information about the data set ◮ we take the first M base vectors and obtain a hyperplane H ⊂ R n · m Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Idea ◮ treat each of the face images (size n × m ) as vector � v ∈ R n · m ◮ find relationships between dimensions by calculating the covariances of the training faces ◮ calculate the eigenvectors of that covariance matrix and sort them descending according to their eigenvalues ◮ new base, in which the i -th base vector contains i -th most information about the data set ◮ we take the first M base vectors and obtain a hyperplane H ⊂ R n · m ◮ H is the M -dimensional face space, all face vectors lie very close to it Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software The faceness-test ◮ face vector � v lies close to H ⇔ their distance is small Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software The faceness-test ◮ face vector � v lies close to H ⇔ their distance is small ◮ but what is the distance between � v and H ? Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software The faceness-test ◮ face vector � v lies close to H ⇔ their distance is small ◮ but what is the distance between � v and H ? ◮ it is the (Euclidian) distance between � v and its projection � v H ⊂ H Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software The faceness-test ◮ face vector � v lies close to H ⇔ their distance is small ◮ but what is the distance between � v and H ? ◮ it is the (Euclidian) distance between � v and its projection � v H ⊂ H ◮ problem: � v and � v H have different bases Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software The faceness-test ◮ face vector � v lies close to H ⇔ their distance is small ◮ but what is the distance between � v and H ? ◮ it is the (Euclidian) distance between � v and its projection � v H ⊂ H ◮ problem: � v and � v H have different bases v H back to R n · m and then calculate the ◮ solution: transform � distance Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Which parameters are interesting? We have to analyze what impacts ◮ the number of images in the database M , Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Which parameters are interesting? We have to analyze what impacts ◮ the number of images in the database M , ◮ the number of pricipal components M ′ to choose, Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Which parameters are interesting? We have to analyze what impacts ◮ the number of images in the database M , ◮ the number of pricipal components M ′ to choose, ◮ the comparison algorithm which test the simiarity between original and reconstructed image, Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Which parameters are interesting? We have to analyze what impacts ◮ the number of images in the database M , ◮ the number of pricipal components M ′ to choose, ◮ the comparison algorithm which test the simiarity between original and reconstructed image, ◮ and the size of the images in the database have. Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Tests All in all, we want to know how the PCA reacts on ◮ images which contain faces from the database, Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Tests All in all, we want to know how the PCA reacts on ◮ images which contain faces from the database, ◮ images which contain faces not in the database, Ramin Marx ACP based face detection

Introduction Analysis Experiments PCA Questions and conclusions Parameters Face Detection Program Used Papers/Software Tests All in all, we want to know how the PCA reacts on ◮ images which contain faces from the database, ◮ images which contain faces not in the database, ◮ and on images which contain no faces at all. Ramin Marx ACP based face detection