## Wednesday, January 21, 2015

### Face Recognition

Chapter 1: Introduction

1. Preliminary

In this project, we implemented a face recognition system by using principal component analysis, which is known as PCA. PCA method provide a mathematical way to reduce the dimension of problem.

Since the most elements of a facial image are highly correlated, it is better to extract a set of interesting and discriminative feature of a facial image. Mathematically speaking, we transform the correlated data to independent data. To implement the transform, we employed some linear algebra method such as SVD (Chapter3). The main idea is to obtain Eigenfaces that every face can be regard as a linear combination of these eigenfaces (Chapter4). Then the face recognition problem convert to a mathematic problem: what is the linear combination of a face? In other words, it simplify a problem from 2D to 1D.

## Friday, January 16, 2015

### Introduction to Spectral Mesh Analysis Toward a simple implementation in C++

Hi reader, as I said before I want to share what I have done and learned from the Vision & Robotics master program. This post includes the our semester project. Actually, the code was given to all the students and the professor wanted us to improve his code and apply 'Spectral Mesh Analysis' on it. You can find more information from the project link that is end of the post. If you have any question free to shoot. I'm just putting here the graphical user interface and how to use the program that is already inside of the project report.

6.1 Framework choice

Because of the constraints that the project must be developed using C++ under Qt IDE, we used the Qt Widgets that are mature and feature rich user interface elements suitable for mostly static user interfaces. Besides, since Qt Widget are native C++ elements it is easier to merge UI with the application logic. The application UI is connected to all other parts of the application through the class Logic. The logic handles all the data interchange between the UI and algorithms. So it is possible to split the application in separate parts.

6.2 Basic elements of UI

The UI is straightforward and easy to use. There are two main parts – openGL screen and sidebar. It is possible to extract many cases of the application usage from the task: