基于KLT算法的MATLAB人脸识别实例分析

找到需要识别的脸

faceDetector = vision.CascadeObjectDetector();

读取一段录像并从中获取需识别的脸

videoFileReader = vision.VideoFileReader(‘tilted_face.avi’);

videoFrame = step(videoFileReader);

bbox = step(faceDetector, videoFrame);

在被检测的脸周围构建框架

videoFrame = insertShape(videoFrame, ‘Rectangle’, bbox);

figure; imshow(videoFrame); title(‘Detected face’);

将框架转化为一系列的4个点，即使被识别的脸发生旋转也能够被看到

bboxPoints = bbox2points(bbox(1, :));



在脸部区域识别特征点

points = detectMinEigenFeatures(rgb2gray(videoFrame), ‘ROI’, bbox);

（指定被保留特征值的最小值,一般为1 ）

显示特征点

figure, imshow(videoFrame), hold on, title(‘Detected features’);

plot(points);



创建一个点的踪迹将错误进行双向限制，以使它即使在噪声存在时也能正常表示

pointTracker = vision.PointTracker(‘MaxBidirectionalError’, 2);

用初始化框架的点的位置初始化踪迹

points = points.Location;

initialize(pointTracker, points, videoFrame);

初始化一个视频播放器显示结果

videoPlayer = vision.VideoPlayer(‘Position’,…

[100 100 [size(videoFrame, 2), size(videoFrame, 1)]+30]);

复制点用于之前的点和现在的框架之间进行几何转换

oldPoints = points;

while ~isDone(videoFileReader)

创建下一个框架

videoFrame = step(videoFileReader);

跟随点的踪迹，注意有几个点可能会丢失

[points, isFound] = step(pointTracker, videoFrame);

visiblePoints = points(isFound, :);

oldInliers = oldPoints(isFound, :);

if size(visiblePoints, 1) >= 2 % need at least 2 points

在之前的点和新的点之间进行几何转换并建立边界线

[xform, oldInliers, visiblePoints] = estimateGeometricTransform(…

oldInliers, visiblePoints, ‘similarity’, ‘MaxDistance’, 4);

运行边界点的转换

bboxPoints = transformPointsForward(xform, bboxPoints);

在被追随的物体周围插入边界框

bboxPolygon = reshape(bboxPoints’, 1, []);

videoFrame = insertShape(videoFrame, ‘Polygon’, bboxPolygon, …

‘LineWidth’, 2);

（reshape表示重新调整矩阵的行数、列数、维数）

显示被追随的点

videoFrame = insertMarker(videoFrame, visiblePoints, ‘+’, …

‘Color’, ‘white’); （在图像或视频中插入标记）

重置点

oldPoints = visiblePoints;

setPoints(pointTracker, oldPoints); （设置跟随点，用于点需要重新检测且很多点丢失在跟踪过程中，可以使用此方法。）

end

用视频播放器显示被注释的视频框架

step(videoPlayer, videoFrame);

end

清空

release(videoFileReader);

release(videoPlayer);

release(pointTracker);