Derived from karhunen-loeve's transformation. Given an s-dimensional vector representation of each face in a training set of images, principal component analysis (PCA) tends to find a t-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space.Examples of cognitive learning in children this new subspace is normally lower dimensional (t<

Independent component analysis (ICA) minimizes both second-order and higher-order dependencies in the input data and attempts to find the basis along which the data (when projected onto them) are - statistically independent .Examples of cognitive learning in children bartlett et al. Provided two architectures of ICA for face recognition task: architecture I - statistically independent basis images, and architecture II - factorial code representation.Examples of cognitive learning in children

Linear discriminant analysis (LDA) finds the vectors in the underlying space that best discriminate among classes. For all samples of all classes the between-class scatter matrix S band the within-class scatter matrix S W are defined.Examples of cognitive learning in children the goal is to maximize S bwhile minimizing S W, in other words, maximize the ratio det| S B|/det| S W| . This ratio is maximized when the column vectors of the projection matrix are the eigenvectors of ( S W^-1 × S B).Examples of cognitive learning in children

An eigenspace-based adaptive approach that searches for the best set of projection axes in order to maximize a fitness function, measuring at the same time the classification accuracy and generalization ability of the system.Examples of cognitive learning in children because the dimension of the solution space of this problem is too big, it is solved using a specific kind of genetic algorithm called evolutionary pursuit (EP).Examples of cognitive learning in children

Elastic bunch graph matching (EBGM). All human faces share a similar topological structure. Faces are represented as graphs, with nodes positioned at fiducial points. (exes, nose...) and edges labeled with 2-D distance vectors.Examples of cognitive learning in children each node contains a set of 40 complex gabor wavelet coefficients at different scales and orientations (phase, amplitude). They are called "jets".Examples of cognitive learning in children recognition is based on labeled graphs. A labeled graph is a set of nodes connected by edges, nodes are labeled with jets, edges are labeled with distances.Examples of cognitive learning in children

An active appearance model (AAM) is an integrated statistical model which combines a model of shape variation with a model of the appearance variations in a shape-normalized frame.Examples of cognitive learning in children an AAM contains a statistical model of the shape and gray-level appearance of the object of interest which can generalize to almost any valid example.Examples of cognitive learning in children matching to an image involves finding model parameters which minimize the difference between the image and a synthesized model example projected into the image.Examples of cognitive learning in children

Human face is a surface lying in the 3-D space intrinsically. Therefore the 3-D model should be better for representing faces, especially to handle facial variations, such as pose, illumination etc.Examples of cognitive learning in children blantz et al. Proposed a method based on a 3-D morphable face model that encodes shape and texture in terms of model parameters, and algorithm that recovers these parameters from a single image of a face.Examples of cognitive learning in children

Given a set of points belonging to two classes, a support vector machine (SVM) finds the hyperplane that separates the largest possible fraction of points of the same class on the same side, while maximizing the distance from either class to the hyperplane.Examples of cognitive learning in children PCA is first used to extract features of face images and then discrimination functions between each pair of images are learned by svms.

Hidden markov models (HMM) are a set of statistical models used to characterize the statistical properties of a signal.Examples of cognitive learning in children HMM consists of two interrelated processes: (1) an underlying, unobservable markov chain with a finite number of states, a state transition probability matrix and an initial state probability distribution and (2) a set of probability density functions associated with each state.Examples of cognitive learning in children

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