fix the code.I have order ID: #661867196 but I have problem

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Face detection
: Face recognition algorithm

The above illustration shows the face detection algorithms using MATLAB.
To indicate the question of enthusiasm as ‘nose’, the contention “Nose” is passed. vision.CascadeObjectDetector(‘Nose’,’MergeThreshold’,16);
The default sentence structure for Nose location :
vision.CascadeObjectDetector(‘Nose’);
In light of the info picture, we can alter the default estimations of the parameters go to vision.CascaseObjectDetector. Here the default esteem for “MergeThreshold” is 4.
At the point when default esteem for “MergeThreshold” is utilized, the outcome is not right.
Here there are more than one location on Hermione.
rgb_label = repmat(label,[1 1 3]);
segmented_images = zeros([size(fabric), nColors],’uint8′);
for check = 1:nColors
shading = texture;
color(rgb_label ~= color_labels(count)) = 0;
segmented_images(:,:,:,count) = shading;
end
stack area organizes;
nColors = 6;
sample_regions = false([size(fabric,1) size(fabric,2) nColors]);
for check = 1:nColors
sample_regions(:,:,count) = roipoly(fabric,region_coordinates(:,1,count),…
region_coordinates(:,2,count));
end
imshow(sample_regions(:,:,2)),title(‘sample area for red’);
clear vid
Granulation FACE
% Initialize stockpiling for each example zone.
colorNames = { “red”,”green”,”purple”,”blue”,”yellow” };
nColors = length(colorNames);
sample_regions = false([imageHeight imageWidth nColors]);
% Select each example zone.

for count = 1:nColors
.Name = [‘Select test area for ” colorNames{count}];
sample_regions(:,:,count) = roipoly(fabric); end
close(f);
% Display a case area.
imshow(sample_regions(:,:,1)) title([‘sample area for ” colorNames{1}]);
Descriptor texture
a = lab_fabric(:,:,2);
b = lab_fabric(:,:,3);
color_markers = zeros([nColors, 2]);
for check = 1:nColors
color_markers(count,1) = mean2(a(sample_regions(:,:,count)));
color_markers(count,2) = mean2(b(sample_regions(:,:,count)));
end
imshow(segmented_images(:,:,:,2)), title(‘red objects’);
Weber images representation law descriptor
rgb_label = repmat(label,[1 1 3]);
segmented_images = zeros([size(fabric), nColors],’uint8′);
for check = 1:nColors
shading = texture;
color(rgb_label ~= color_labels(count)) = 0;
segmented_images(:,:,:,count) = shading;
end
Gender recognition
Gender recognition is one of the numerous biometric distinguishing methods for perceiving and confirming appearances from a picture. By utilizing face recognition calculation, a Principal ComponentAnalysis (PCA) is used to distinguish the places of appearances in a picture. Then, facial components are extricated from the part of the picture. Lastly, information relating to facial elements are removed and contrasted. The facial elements are then put in a face database to locate the most similar one from the recognition(Bin, Musician, Kit and John 42). The primary research done on face acknowledgment was in 1960 by Woodrow Wilson (Woody) Bledsoe, the pioneer of face acknowledgment (Révay, 24). The face acknowledgment program made by Bledsoe was not ready to recognize the position of facial components without the guide from the human. Considerable attributes of facial acknowledgment must be put into consideration when designing any aspect of gender recognition
% Access a Matrox(R) frame grabber attached to a Pulnix TMC-9700 camera, and
% acquire data using an NTSC format.
% vidobj = videoinput(‘matrox’,1,’M_NTSC_RGB’);
% Open a live preview window. Point camera onto a piece of colorful fabric.
% preview(vidobj);
% Capture one frame of data.
% fabric = getsnapshot(vidobj);
% imwrite(fabric,’fabric.png’,’png’);
% Delete and clear associated variables.
% delete(vidobj)
% clear vidobj;
fabric = imread(‘fabric.png’);
figure(1), imshow(fabric), title(‘fabric’);
Conclusion
The methods of gender recognition are divided into four classes; the element invariant methodologies, layout coordinating methodologies, learning based methodologies and appearance-based methodologies (Yang and Ahuja, 219). Invariant methodology finds facial components that are invariant to face edge, position, posture and lighting condition. Layout coordinating methodologies utilizes pre-chosen confronts as formats to contrast and give information about the picture. The learning based methodologies utilize principles and certainty about human appearances to model facial elements, for instance, a face comprises of match of symmetric eyes, a nose underneath the eyes and the mouth at the base. The appearance-based methodology is like layout coordinating methodologies; it utilizes pre-marked arrangements of pictures to prepare or determine design database. These can either be used to contrast or give information about the picture (Dorf and Robert, 32). A face recognition system utilizes human skin shading models. The skin shading territory in the picture, as illustrated above, utilizes the skin shading models. Such an approach makes it possible to find the accurate face area.

References
Bin Abdul Rahman, Nusirwan Anwar, Kit Chong Wei, and John See. “Rgb-h-cbcr skin color model for human face detection.” Faculty of Information Technology, Multimedia University (2007).
Top of Form
Dorf, Richard C, and Robert H. Bishop. Modern Control Systems. Menlo Park, Calif. [u.a.: Addison-Wesley, 1998. Print.Bottom of Form
Révay, Zs, et al. “Construction and characterization of the redesigned PGAA facility at The University of Texas at Austin.” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 577.3 (2007): 611-618.