STEP 1 ------ Extract faces by running Face_detection_image_cropping.m on all the images in the folder. Resulting 250×250 Images are saved in ./LFW_Cropping/Training/ Requires Image processing toolbox Requires ./LFW/ directory containing male images Requires ./LFW_dataset/Training/female directory containing female images - Creates ./LFW_Cropping/Training/male/ directory containing the extracted male faces - Creates ./LFW_Cropping/Training/female/ directory containing the extracted female faces STEP 2 ------ Preprocess each image by running Face_image_reading_and_preprocessing.m Requires Image processing toolbox Requires folder ./LFW_Cropping/Training/, which it assumes contains subdirectores male/ and female/ Preprocessing consists of 1. Conversion to Grayscale 2. Histogram equalization (normalization) 3. Scale to 80×80 4. convert to vectors of 6400 columns 5. Each vector saved as row of matrix X 6. Labels are stored in linear vector Y Y[i] = 1 if X[i,:] corresponds to a male image Y[i] = 2 if X[i,:] corresponds to a female image 7. Creates ./LFW_faces.mat containing X and Y STEP 3 ------ run compute_PCA Requires ./LFW_faces.mat Creates ./LFW_Face Detection/
lchanmann/Gender_Classification
Apply Supervised Learning algorithms (i.e Naive Bayes, KNN and SVM) with boosting on PCA-ed face image dataset
MATLAB