face recognition and detection

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powerpoint presentation * face recognition and detection cse 576, spring 2008 face recognition and detection * recognition problems what is it? object and scene recognition who is it? identity recognition where is it? object detection what are they doing? activities all of these are classification problems choose one class from a list of possible candidates face recognition and detection cse 576, spring 2008 face recognition and detection * what is recognition? a different taxonomy from [csurka et al. 2006]: recognition where is this particular object? categorization what kind of object(s) is(are) present? content-based image retrieval find me something that looks similar detection locate all instances of a given class face recognition and detection cse 576, spring 2008 face recognition and detection * readings c. bishop, “neural networks for pattern recognition”, oxford university press, 1998, chapter 1. forsyth and ponce, chap 22.3 (through 22.3.2--eigenfaces) turk, m. and pentland, a. eigenfaces for …

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on * skin detection skin pixels have a distinctive range of colors corresponds to region(s) in rgb color space skin classifier a pixel x = (r,g,b) is skin if it is in the skin (color) region how to find this region? skin face recognition and detection cse 576, spring 2008 face recognition and detection * skin detection learn the skin region from examples manually label skin/non pixels in one or more “training images” plot the training data in rgb space skin pixels shown in orange, non-skin pixels shown in gray some skin pixels may be outside the region, non-skin pixels inside. face recognition and detection cse 576, spring 2008 face recognition and detection * skin classifier given x = (r,g,b): how to determine if it is skin or not? nearest neighbor find labeled pixel closest to x find plane/curve that separates the two classes popular approach: support vector machines (svm) …

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common choice is rotated gaussian center covariance face recognition and detection cse 576, spring 2008 face recognition and detection * learning conditional pdf’s we can calculate p(r | skin) from a set of training images but this isn’t quite what we want why not? how to determine if a pixel is skin? we want p(skin | r) not p(r | skin) how can we get it? face recognition and detection cse 576, spring 2008 face recognition and detection * bayes rule in terms of our problem: what can we use for the prior p(skin)? domain knowledge: p(skin) may be larger if we know the image contains a person for a portrait, p(skin) may be higher for pixels in the center learn the prior from the training set. how? p(skin) is proportion of skin pixels in training set what we measure (likelihood) domain knowledge (prior) what we want (posterior) normalization term …

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n: boosting [viola & jones] face recognition and detection eigenfaces for recognition matthew turk and alex pentland j. cognitive neuroscience 1991 cse 576, spring 2008 face recognition and detection * linear subspaces classification can be expensive: big search prob (e.g., nearest neighbors) or store large pdf’s suppose the data points are arranged as above idea—fit a line, classifier measures distance to line what does the v2 coordinate measure? what does the v1 coordinate measure? distance to line use it for classification—near 0 for orange pts position along line use it to specify which orange point it is convert x into v1, v2 coordinates face recognition and detection cse 576, spring 2008 face recognition and detection * dimensionality reduction dimensionality reduction we can represent the orange points with only their v1 coordinates (since v2 coordinates are all essentially 0) this makes it much cheaper to store and compare points a bigger …

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n and detection * the space of faces an image is a point in a high dimensional space an n x m image is a point in rnm we can define vectors in this space as we did in the 2d case + = face recognition and detection cse 576, spring 2008 face recognition and detection * dimensionality reduction the set of faces is a “subspace” of the set of images we can find the best subspace using pca this is like fitting a “hyper-plane” to the set of faces spanned by vectors v1, v2, ..., vk any face face recognition and detection cse 576, spring 2008 face recognition and detection * eigenfaces pca extracts the eigenvectors of a gives a set of vectors v1, v2, v3, ... each vector is a direction in face space what do these look like? face recognition and detection cse 576, spring 2008 face …

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powerpoint presentation * face recognition and detection cse 576, spring 2008 face recognition and detection * recognition problems what is it? object and scene recognition who is it? identity recognition where is it? object detection what are they doing? activities all of these are classification problems choose one class from a list of possible candidates face recognition and detection cse 576, spring 2008 face recognition and detection * what is recognition? a different taxonomy from [csurka et al. 2006]: recognition where is this particular object? categorization what kind of object(s) is(are) present? content-based image retrieval find me something that looks similar detection locate all instances of a given class face recognition and detection cse 576, spring 2008 face recognition an...

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