Motivation and Purpose:
What technology is best suited to determine people’s identity? There are many different identification technologies available, many of which have been in commercial use for years. The most common person verification and identification methods today are Password/PIN known as Personal Identification Number, systems. The problem with that or other similar techniques is that they are not unique, and is possible for somebody to forget loose or even have it stolen for somebody else. In order to overcome these problems there has developed considerable interest in "biometrics" identification systems, which use pattern recognition techniques to identify people using their characteristics. Some of those methods are fingerprints and retina and iris recognition. Though these techniques are not easy to use. For example in bank transactions and entry into secure areas, such technologies have the disadvantage that they are intrusive both physically and socially. The user must position the body relative to the sensor, and then pause for a second to declare himself or herself.
Face recognition from video and voice recognition have a natural place in these next generation smart environments, they are unobtrusive, are usually passive, do not restrict user movement, and are now both low power and inexpensive.
Face Recognition is a part of a project in TUBITAK BILTEN. In our summer practice we worked on face detection and we therefore focused our
research towards recognition of faces. Our goal at the end of the project is to develop a face recognition program which works efficiently and simply. In addition to this, we are planning to improve the algorithm by using neural networks and separation of training classes.
Our aim is to build a face recognizer that works under varying poses, the difficult part of which is to handle face rotations in depth. So, our recognizer is planned to be able to identify faces which are rotated in y axis, too.
Our subject is to implement a system which recognizes faces using eigenface method. The system will take an image as input and try to find a match in its database. Furthermore, the algorithm will be improved by using neural network and classification of the face database.
The work to be performed consists of two parts. First part is the construction of eigenfaces. Second part is the identification of the given image i.e if there is a match in the database(known faces). The algorithm will be implemented in Windows environment using Visual C++.
In Turkey, TUBITAK BILTEN’s Signal Processing group works on a project called GAYE which includes face detection and recognition. The project will be used in security systems.
In the world, face recognition and detection is used as a research project in universities. Furthermore, there exist many companies working on biometric solutions for security. The equipment used for such security systems consists of a digital camera and a PC connected to the camera. More information about these products can be reached from the face recognition home page (http://www.cs.rug.nl/~peterkr/FACE/face.html).
Face recognition rises from the moment that machine started to become more and more "intelligent" and had the advance of fill in, correct or help the lack of human abilities and senses. The subject of face recognition is as old as computer vision and both because of the practical importance of the topic and theoretical interest from cognitive science.
The methods used for face recognition are :
Eigenface method, template matching, graph matching, linear subspace method, neural network method and fisherface method.
The eigenface approach applies the Karhonen-Loeve transform for feature extraction. It greatly reduces the facial feature dimension and yet maintains reasonable discriminating power.
The neural network approach , though some variants of the algorithm work on feature extraction as well, mainly provides sophisticated modeling scheme for estimating likelihood densities in the pattern recognition phase.
The template matching method operates by performing direct correlation of image segments. Template matching is only effective when the query images have the same scale, orientation and illumination as the training images.
In graph matching method , objects are represented with sparse graphs whose vertices are labeled with a multi-resolution description in terms of a local power spectrum and whose edges are labeled with geometrical distances.
In the linear subspace method for each face three or more images are taken under different lighting directions. By using these 3 images , a 3D basis for the linear subspace is constructed.
To perform recognition, the distance of a new image to each linear subspace is calculated and the face corresponding to the shortest distance is chosen.
Fisherface method is a refinement of the eigenface algorithm. It further reduces the eigenspace by the fisher’s linear discriminant(FLD). FLD selects the subspace in such a way that the ratio of the between-class scatter and the within-class scatter is maximized. It is reported that the Fisherface algorithm outperforms the eigenface algorithm on the facial database with wide variations in lighting conditions .
As correlation methods are computationally expensive and require great amounts of storage, it is natural to pursue dimensionality reduction schemes. A technique now commonly used for dimensionality reduction in computer vision-
particularly in face recognition- is Principal Component Analysis(PCA). PCA techniques, also known as Karhonen-Loeve methods , choose a dimensionality reducing linear projection that maximizes the scatter of all projected samples.
Eigenface method is based on an information theory approach that decomposes face images into a small set of characteristic feature images called eigenfaces, which may be thought as the principal components of the initial training set of face images. Recognition is performed by projecting a new image into the subspace spanned by the eigenfaces and then classifying the face by comparing its position in the face space with the positions of the known individuals.
1-) Project Proposal Report(10.10.2001)
2-) First prototype finished. (20.10.2001)
3-) Project Progress Report
4-) Last prototype finished. (1.12.2001)
5-) Final report finished.
6-) Demo of the program
WP1: Analysis Phase
Task1:Understanding the main idea behind eigenfaces
Task2:Survey about eigenfaces
Task3:Proposal Report writing
Task1: Main design of the program.
Task2 :Design of the user interface
Task3: Implementation of the first part(eigenface construction)
Task4: Progress report writing
Task5: Implementation of the second part (identification of faces)
WP3: Final Part
Task1: Testing the program
Task2: Collecting statistical information about the program
Task3: Final report writing
A PC with Windows 98 operating system.
Microsoft Visual Studio
An image processing tool( like Adobe Photoshop)
 Turk, M., and Pentland, A., “Eigenfaces for Recognition”, Journal of Neuroscience, vol.3, no1 1991
 Belhumeur, P., Hespanha, J. , and Kriekman, D., “Eigenfaces vs. Fisherfaces : Recognition using class specific linear projection”, IEEE Trans. Pattern Analysis Machine Intelligence, vol.19 ,no.7, pp.711-720, July 1997
 Pentland , A., Moghaddam, B., Starner, T., and Turk, M., “View-based and Modular Eigenspaces for Face Recognition” , proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, pp.84-91, Seattle, WA,1994
Project Manager: Hasan Dogu
Project Coordinator: Ilknur Kaynar
Research Coordinator: Ilknur Kaynar
Implementation Manager: Hasan Dogu