I Have completed Deblurring by using wiener Filter and now i want to get facial angle and emotion of a persons

I = im2double(imread('greyface1.jpg'));
imshow(I);
title('Original  Image');

figure();
LEN = 45;
THETA = 15;
PSF = fspecial('motion', LEN, THETA);
blurred = imfilter(I, PSF, 'conv', 'circular');
imshow(blurred);
title('Blurred Image');

figure();
wnr1 = deconvwnr(blurred, PSF, 0);
imshow(wnr1);
title('Restored Image');

figure();
noise_mean = 0;
noise_var = 0.0001;
blurred_noisy = imnoise(blurred, 'gaussian', noise_mean, noise_var);
imshow(blurred_noisy);
title('Simulate Blur and Noise');

figure();
wnr2 = deconvwnr(blurred_noisy, PSF, 0);
imshow(wnr2);
title('Restoration of Blurred, Noisy Image Using NSR=0');

figure();
signal_var = var(I(:));
% Corrected part: Convert to double before using var
uniform_quantization_var = (1/256)^2/12;
wnr5 = deconvwnr(blurred_noisy, PSF, uniform_quantization_var / signal_var);
imshow(wnr5);
title('Restoration of Blurred, Quantized Image Using Computed NSR');

[Kartikeya]

After successfully implementing deblurring using the Wiener filter, my next objective is to determine facial angles and emotions of individuals. Can anyone share code or steps to follow

Answer 1

[Image Analyst]

It's a big, evolving field and of course there are some faces where the emotion is ambiguous or uncertain. Anyway, here are a lot of papers on it. Pick on and code it up. Be forewarned though that the solution is not going to be some simple 300 line script. It will likely be very complex.

• 21.3.6 Facial Expressions and Emotion Analysis and Description
• 21.3.6.1 Face Expression Recognition and Analysis, Facial Expression Analysis, Facial Expressions
• 21.3.6.1.1 Face Expression Recognition Using Learning, Neural Nets
• 21.3.6.1.2 Sptatio-Temporal Analysis for Face Expression Recognition
• 21.3.6.1.3 Face Expression Recognition for Human Comupter Interaction
• 21.3.6.1.4 Applied Face Expression Recognition, Specific Expressions
• 21.3.6.1.5 Micro Expressions, Subtle Expressions, Face Expression Recognition
• 21.3.6.1.6 Face Expression Recognition for Stress
• 21.3.6.1.7 Face Expression Recognition for Pain
• 21.3.6.1.8 Three-Dimensional Face Expression Recognition and Analysis
• 21.3.6.1.9 Multi-View Face Expression Recognition and Analysis
• 21.3.6.1.10 Face Action Units for Expressions and Motion Analysis, FAU, FACS
• 21.3.6.1.11 Facial Feature Tracking for Expressions
• 21.3.6.1.12 Face Expression Recognition from a Single Image
• 21.3.6.2 Emotion Recognition from Face Images
• 21.3.6.2.1 Emotion Recognition, Deep Learning
• 21.3.6.2.2 Emotion Recognition, Survey, General, Review, Datasets, Database
• 21.3.6.2.3 Emotion Recognition, Body Gestures, Body Language
• 21.3.6.2.4 Depression Analysis, PTSD, Mental Health
• 21.3.6.2.5 Personality, Traits, Mood, Deception, Diagnosis Analysis
• 21.3.6.2.6 Emotion Recognition Using EEG Analysis, Electroencephalogram
• 21.3.6.2.7 Audio-Visual Emotion, Audiovisual Emotion Recognition
• 21.3.6.2.8 Multi-Modal Emotion, Multimodal Emotion Recognition
• 21.3.6.2.9 Emotion Recognition, from Other Than Faces
• 21.3.6.3 Facial Expressions, Overviews, Surveys, Data