计算机/数字图像处理专业本科毕设——人脸识别系统

Face Recognition with Python

Philipp Wagner

http://www.bytefish.de

July 18, 2012

Contents

1 Introduction 1

2 Face Recognition 2

2.1 Face Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.1.1 Reading the images with Python . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Conclusion 15

1 Introduction

In this document I’ll show you how to implement the Eigenfaces [13] and Fisherfaces [3] method with

Python, so you’ll understand the basics of Face Recognition. All concepts are explained in detail, but a

basic knowledge of Python is assumed. Originally this document was a Guide to Face Recognition with

OpenCV. Since OpenCV now comes with the cv::FaceRecognizer, this document has been reworked

into the official OpenCV documentation at:

• http://docs.opencv.org/trunk/modules/contrib/doc/facerec/index.html

I am doing all this in my spare time and I simply can’t maintain two separate documents on the

same topic any more. So I have decided to turn this document into a guide on Face Recognition with

– Tutorial On Saving & Loading a FaceRecognizer

By the way you don’t need to copy and paste the code snippets, all code has been pushed into my

github repository:

• github.com/bytefish

• github.com/bytefish/facerecognition guide

Everything in here is released under a BSD license, so feel free to use it for your projects. You

are currently reading the Python version of the Face Recognition Guide, you can compile the GNU

Octave/MATLAB version with make octave.

1

2 Face Recognition

angles or movement. Since we don’t see the world as scattered pieces, our visual cortex must somehow

combine the different sources of information into useful patterns. Automatic face recognition is all

about extracting those meaningful features from an image, putting them into a useful representation

and performing some kind of classification on them.

Face recognition based on the geometric features of a face is probably the most intuitive approach to

face recognition. One of the first automated face recognition systems was described in [9]: marker

points (position of eyes, ears, nose, ...) were used to build a feature vector (distance between the

points, angle between them, ...). The recognition was performed by calculating the euclidean distance

between feature vectors of a probe and reference image. Such a method is robust against changes in

illumination by its nature, but has a huge drawback: the accurate registration of the marker points

classification becomes easy. The lower-dimensional subspace is found with Principal Component

Analysis, which identifies the axes with maximum variance. While this kind of transformation is

optimal from a reconstruction standpoint, it doesn’t take any class labels into account. Imagine a

situation where the variance is generated from external sources, let it be light. The axes with maximum

variance do not necessarily contain any discriminative information at all, hence a classification becomes

impossible. So a class-specific projection with a Linear Discriminant Analysis was applied to face

recognition in [3]. The basic idea is to minimize the variance within a class, while maximizing the

variance between the classes at the same time (Figure 1).

Recently various methods for a local feature extraction emerged. To avoid the high-dimensionality of

the input data only local regions of an image are described, the extracted features are (hopefully) more

images! You can either create your own database or start with one of the available databases, face-

rec.org/databases gives an up-to-date overview. Three interesting databases are

AT&T Facedatabase The AT&T Facedatabase, sometimes also known as ORL Database of Faces,

Yale Facedatabase A The AT&T Facedatabase is good for initial tests, but it’s a fairly easy

2

Extended Yale Facedatabase B The Extended Yale Facedatabase B contains 2414 images of 38

Each directory is given a unique (integer) label, you probably want to store the folder name as well.

The function returns the images and the corresponding classes. This function is really basic and

there’s much to enhance, but it does its job.

Listing 2: src/py/tinyfacerec/util.py

3

2.2 Eigenfaces

The resulting eigenvectors are orthogonal, to get orthonormal eigenvectors they need to be normalized

to unit length. I don’t want to turn this into a publication, so please look into [7] for the derivation

and proof of the equations.

4

2.2.2 Eigenfaces in Python

The observations are given by row, so the projection in Equation 4 needs to be rearranged a little:

5