【图像处理】基于形状提取和模式匹配组合的面部特征点提取方法附Matlab代码

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智能优化算法  神经网络预测 雷达通信  无线传感器

信号处理 图像处理 路径规划 元胞自动机 无人机  电力系统

⛄ 内容介绍

In this paper, we propose a fast combined separabil-ity filter, which can selectively detect circular features such as pupils and nostrils in an image of the human face. The proposed filter is designed as a combination of multiple rectangle separability filters so that it can achieve high-speed processing and high positioning ac-curacy at the same time. The evaluation experiments using synthetic images and real face images show that the proposed filter is 70 times faster than the conven-tional circular separability filter.

⛄ 部分代码

function MAP = cvtCombSimpRectFilter45(I,P,sh)

% Function to generate separability map for diagonal filters (diagonal left and right)

% Input: I: 45 degrees integral image, obtained by using cvtIntegralImage45(X);

%        P: 45 degrees integral image of the square pixel values, obtained by using cvtIntegralImage45(X.^2);

%        sh: size of the filter

% Output: MAP: two separability maps (diagonal left and diagonal right), with size: [Height, Width, 2].

%

% If you use this code, we would appreciate if you cite the following paper(s):

% 

% [1] Y. Ohkawa, C. H. Suryanto, K. Fukui, 

% "Fast Combined Separability Filter for Detecting Circular Objects", 

% The twelfth IAPR conference on Machine Vision Applications (MVA) pp.99-103, 2011.

%

% [2] K. Fukui, O. Yamaguchi, 

% "Facial feature point extraction method based on combination of shape extraction 

%  and pattern matching", Systems and Computers in Japan 29 (6), pp.49-58, 1998.

%

% This code is written by Yasuhiro Ohkawa and distributed under BSD License.

% Computer Vision Laboratory (CVLAB)

% Graduate school of Systems and Information Engineering

% University of Tsukuba

% 2016

%

% Email: tsukuba.cvlab@gmail.com

% HP: http://www.cvlab.cs.tsukuba.ac.jp/

%

r = round(sh/sqrt(2));

w = ceil(sh/3/sqrt(2));

br = 2*r;

MAP(:,:,1) = tmpFnc(I,P,r,br,w,w);

MAP(:,:,2) = tmpFnc(I,P,w,w,r,br);

end

%%

function MAP = tmpFnc(I,P,r,br,w,bw)

MAP = zeros(size(I)-1);

[H, W] = size(MAP);

h = bw+br+2;

N =(2*bw+1)*(2*(1+2*br));

N1=(2*w+1)*(2*(1+2*r));

N2=N-N1;

HH = bw + br +1+1:H-(bw + br +2);

WW = bw + br +3:W-(bw + br+1);

P1 = I(HH - bw - br -1      ,WW + bw - br - 1);

P2 = I(HH + bw - br -1+1    ,WW - bw - br - 1-1  );

P3 = I(HH + bw + br +1      ,WW - bw + br - 1);

P4 = I(HH - bw + br         ,WW + bw + br    );

S = (P4+P2-P3-P1);

P1 = P(HH - bw - br -1      ,WW + bw - br - 1);

P2 = P(HH + bw - br -1+1    ,WW - bw - br - 1-1  );

P3 = P(HH + bw + br +1      ,WW - bw + br - 1);

P4 = P(HH - bw + br         ,WW + bw + br    );

T= (P4+P2-P3-P1);

M = S./N;

Y = T./N;

St = Y - M.^2;

P1 = I(HH - w - r -1      ,WW + w - r - 1);

P2 = I(HH + w - r -1+1    ,WW - w - r - 1-1  );

P3 = I(HH + w + r +1      ,WW - w + r - 1);

P4 = I(HH - w + r         ,WW + w + r    );

S1= (P4+P2-P3-P1);

S2=S-S1;

M1=S1/N1;

M2=S2/N2;

Sb = ((N1*((M1-M).^2)) + (N2*((M2-M).^2)))/N;

MAP(h:end-h,h:end-h)=(Sb./St).*sign(M2-M1);

MAP(isnan(MAP))=0;

MAP(isinf(MAP))=0;

end

⛄ 运行结果

⛄ 参考文献

[1]谢琛. 基于主动形状模型的人脸特征提取技术研究与应用[D]. 国防科学技术大学, 2009.

[2] Y. Ohkawa, C. H. Suryanto, K. Fukui, "Fast Combined Separability Filter for Detecting Circular Objects", The twelfth IAPR conference on Machine Vision Applications (MVA) pp.99-103, 2011.​

[3] K. Fukui, O. Yamaguchi, "Facial feature point extraction method based on combination of shape extraction and pattern matching", Systems and Computers in Japan 29 (6), pp.49-58, 1998.

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