Face recognition using convolutional macropixel comparison approach

Convolutional Neural Network (CNN) is a widely used deep learning framework and is applied in the field of face recognition achieving outstanding results. Macropixel Comparison Approach is a shallow mathematical approach that recognizes face by comparing original pixel blocks of face images. In this thesis, we are inspired by ideas of the currently popular deep neural network framework and introduce two features into the mathematical approach: deep overlap and weighted filter. The aim is exploring if the idea of deep learning could benefit mathematical method which might extends the scope of face recognition research. Results from our experiments show that the new proposed approach achives markedly better recognition rates than the original macropixel method.