Description
The neural network in machine learning facilitates automation in image processing facilities which is either number plate recognition, face detection, expression recognition, joining images at different exposures etc. It also eases the task to recognize and verify the ages from the images which help in passports and other related applications. This MATLAB code for age detection from images contributes towards that fact. We developed this code at free-thesis. We used FGNET aging dataset for the features generation and testing. Though testing also worked on other images that don’t have covered faces by sunglasses or by any other means. The complete pipeline is like this:
files=dir('./FGNET/images'); for ii=3:size(files,1) imgname=files(ii).name; img=imread(['./FGNET/images/',imgname]); if size(img,3)==3 img=rgb2gray(img); end features(ii,:) = extractLBPFeatures(img); end
After extracting features we had a matrix of dimension 1002*20 where each row represents every image in the database. The interesting step in the whole pipeline is the features pre-processing. The whole database is divided into eight different age groups, which makes this data highly imbalanced as shown in figure 2.
The difference in the number of samples from the major class is used to balance for every class. The code snippet for this is as
%add on samples will be calculated by taking the difference of each %classSamples with highest number of class samples [maximumSamples,sampleClass]=max(classNo); % number of maximum samples for ii=1:numel(class) samplediff(ii)=maximumSamples-classNo(ii); N (ii) = ceil(samplediff(ii)/ 100); end
The samples distribution in each class after the SMOTE algorithm is shown in the figure.
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