视频中的人脸识别和追踪

本文演示了如何利用Matlab对视频中的人脸进行识别。代码中主要运用ViolaJones算法，会对识别的眼部，鼻部，嘴部等进行点标注。

代码来源：

https://cn.mathworks.com/examples/matlab-computer-vision/mw/vision-ex10252853-face-detection-and-tracking-using-the-klt-algorithm

相应的工具箱下载：

https://cn.mathworks.com/matlabcentral/profile/authors/3215751-mathworks-image-acquisition-toolbox-team

录制结果

代码部分

% Detect objection by reading a video

clc

clear

% Create a cascade detector object.

faceDetector = vision.CascadeObjectDetector();

% Read a video frame and run the face detector.

videoFileReader = vision.VideoFileReader('ellen.mov');

videoFrame =step(videoFileReader);

bbox = step(faceDetector, videoFrame);

% Draw the returned bounding box around the detected face.

videoFrame = insertShape(videoFrame, 'Rectangle',bbox);

figure; imshow(videoFrame); title('Detected face');

% Convert the first box into a list of 4 points

% This is needed to be able to visualize the rotation of theobject.

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

% Detect feature points in the face region.

points = detectMinEigenFeatures(rgb2gray(videoFrame), 'ROI', bbox);

% Display the detected points.

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

plot(points);

% Create a point tracker and enable the bidirectional errorconstraint to

% make it more robust in the presence of noise and clutter.

pointTracker = vision.PointTracker('MaxBidirectionalError',2);

% Initialize the tracker with the initial point locations and theinitial

% video frame.

points = points.Location;

initialize(pointTracker, points, videoFrame);

videoPlayer = vision.VideoPlayer('Position',...

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

% Make a copy of the points to be used for computing the geometric

% transformation between the points in the previous and the currentframes

oldPoints = points;

while ~isDone(videoFileReader)

% get the next frame

videoFrame =step(videoFileReader);

% Track the points. Note that some points may be lost.

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

visiblePoints= points(isFound, :);

oldInliers =oldPoints(isFound, :);

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

% Estimate the geometric transformationbetween the old points

% and the new points and eliminate outliers

[xform,oldInliers, visiblePoints] = estimateGeometricTransform(...

oldInliers, visiblePoints, 'similarity','MaxDistance', 4);

% Apply the transformation to the boundingbox points

bboxPoints= transformPointsForward(xform, bboxPoints);

% Insert a bounding box around the objectbeing tracked

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

videoFrame= insertShape(videoFrame, 'Polygon', bboxPolygon, ...

'LineWidth', 2);

% Display tracked points

videoFrame= insertMarker(videoFrame, visiblePoints, '+', ...

'Color', 'white');

% Reset the points

oldPoints= visiblePoints;

setPoints(pointTracker,oldPoints);

end

% Display the annotated video frame using the video playerobject

step(videoPlayer,videoFrame);

end

% Clean up

release(videoFileReader);

release(videoPlayer);

release(pointTracker);

常见问题

程序无法运行，需要下载安装链接2中的相应工具包。下载时需要Matlab账号。

运行时出错，换一个视频或者转换视频格式。