文件列表:

facedetection (0, 2007-11-06)
facedetection\aggregatePoses.m (2063, 2006-06-04)
facedetection\checkEdgeLocation.m (1860, 2006-05-12)
facedetection\checkEdgeScale.m (1681, 2006-06-04)
facedetection\createFilter.m (1188, 2006-05-09)
facedetection\demo.m (3169, 2006-06-04)
facedetection\detectFaces.m (2512, 2006-06-03)
facedetection\drawFace.m (1080, 2006-06-04)
facedetection\drawFaces.m (538, 2006-06-04)
facedetection\drawRectangle.m (600, 2006-05-09)
facedetection\firstRun-2.mat (209315, 2006-05-17)
facedetection\firstRun.m (2033, 2006-05-16)
facedetection\fullRun-2.mat (452811, 2006-05-24)
facedetection\fullRun.m (2168, 2006-06-01)
facedetection\getEdgeMap.m (4381, 2006-05-17)
facedetection\getFlatMap.m (1191, 2006-05-14)
facedetection\getPoseImage.m (1972, 2006-05-14)
facedetection\getPoseLevels.m (746, 2006-05-17)
facedetection\getPoseRelatives.m (618, 2006-05-17)
facedetection\getPoses.m (4861, 2006-05-17)
facedetection\getPoseTrainingSet.m (2252, 2006-05-16)
facedetection\integralFilter.m (1177, 2006-05-09)
facedetection\integralImage.m (266, 2006-05-09)
facedetection\integralPoint.m (518, 2006-05-09)
facedetection\labelImages.m (2328, 2006-05-16)
facedetection\normalizeImages.m (1308, 2006-05-13)
facedetection\sampleImage.jpg (25363, 2006-05-24)
facedetection\test.m (7713, 2006-06-03)
facedetection\testCell.m (1776, 2006-05-17)
facedetection\testImage.m (4732, 2006-06-03)
facedetection\testWindow.m (2732, 2006-06-03)
facedetection\trainCell.m (4881, 2006-05-16)
facedetection\trainCells.m (2663, 2006-05-17)

Fast Face Detection ------------------- Authors: -Mohamed ALY -Jonathan LEE Contents --------- -*.m source files -.mat trained classifiers -sample image -example file Source Files ------------ -aggregatePoses: aggregates all the face detections returned by detectFaces.m in an attempt to eliminate false alarms -checkEdgeLocation: checks if a specific edge (location, type, scale) is present in the input edge map -checkEdgeScale: checks the whole edge map for specific edge type at specific scale (all locations) -detectFaces: checks a given image for faces at different subsampled scales of the image (calls testImage for different scales) -drawFace: draws the input faces detections on the current figure (used to show the triangles of the detected faces) -firstRun: used to train the classifier for the 32x32 window size -fullRun: used to train the classifier for the ***x*** window size -getEdgeMap: generates an edge map for the input image -getFlatMap: generates a flat area map fr the input image -getPoseImage: generates an image with a given pose from an input image (used in the training process to generate the synthetic training set) -getPoseLevels: gets the depth of the pose tree -getPoseRelations: used to get the children and parents of a given pose in the pose tree (used in detection process to navigate the tree) -getPoses: used to generate the pose tree according to the number of splits required and their types (splits on location, scale or tilt) -getPoseTrainingSet: used to generate the synthetic training set for each pose from the original input training set by generating different random orientation, scales and translations -labelImages: used in the normalization of the original input traininig set (ORL database) so label the locations of the eyes and mouth center -normalizeImages: uses the labeled images to normalize the input training set so that all faces have eyes and mouth at the same position (to make generating the synthetic training set systematic) -testCell: used to test an input window for faces according to a specific cell in the pose tree i.e. tests for the number of edges and flat areas in that window and compare to the threshold got from training -testImage: used detect a given image for faces at the same scale of the input image -testWindow: tests a given window of the image going down the pose tree in a breadth first approach, such that it doesnt test a given cell unless its parent responds true -trainCell: the main training algorithm that trains the cell classifier of a given cell in the pose tree -trainCells: called by firstRun or fullRun to generate a subset of cell classifiers Mat files ---------- -firstRun-2.mat: classifier trained using 32x32 window & 8x8 blocks with a pose tree of 5 levels = 245 classifiers (2 splits on location, 1 on scale, 1 on tilt, and 1 on scale) -fullRun-2.mat: classifier trained using ***x*** window & 8x8 blocks with a pose tree of 6 levels = 501 classifiers (2 splits on location, 1 on scale, 1 on tilt, 1 on scale, and 1 on tilt again) -trainSet.mat: the normalized training set of 400 images from ORL database Sample Image ------------ sampleImage.jpg Example script -------------- demo.m

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