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Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Matthew Turk and Alex Pentland beginning in 1987, and is considered the first successful example of facial recognition technology.[citation needed] These eigenvectors are derived from the covariance matrix of the probability distribution of the high-dimensional vector space of possible faces of human beings. Image File history File links Eigenfaces. ...
Image File history File links Eigenfaces. ...
AT&T Labs is the research & development arm of American telecommunications giant, AT&T. AT&T Labs originated in 1996, when AT&T spun-off most of its Bell Labs research business as Lucent Technologies. ...
In linear algebra, the eigenvectors (from the German eigen meaning own) of a linear operator are non-zero vectors which, when operated on by the operator, result in a scalar multiple of themselves. ...
Computer vision is the science and technology of machines that see. ...
A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. ...
A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. ...
MIT Professor Alex (Sandy) Pentland is a pioneer in wearable computers, health systems, smart environments, and technology for developing countries. ...
In linear algebra, the eigenvectors (from the German eigen meaning inherent, characteristic) of a linear operator are non-zero vectors which, when operated on by the operator, result in a scalar multiple of themselves. ...
In statistics and probability theory, the covariance matrix is a matrix of covariances between elements of a vector. ...
In probability theory, every random variable may be attributed to a function defined on a state space equipped with a probability distribution that assigns a probability to every subset (more precisely every measurable subset) of its state space in such a way that the probability axioms are satisfied. ...
2-dimensional renderings (ie. ...
In mathematics, a vector space (or linear space) is a collection of objects (called vectors) that, informally speaking, may be scaled and added. ...
Eigenface generation
To generate a set of eigenfaces, a large set of digitized images of human faces, taken under the same lighting conditions, are normalized to line up the eyes and mouths. They are then all resampled at the same pixel resolution. Eigenfaces can be extracted out of the image data by means of a mathematical tool called principal component analysis (PCA). Here are the steps involved in converting an image of a face into eigenfaces: In mathematics, a set can be thought of as any collection of distinct objects considered as a whole. ...
This article is about the picture element. ...
In statistics, principal components analysis (PCA) is a technique that can be used to simplify a dataset; more formally it is a linear transformation that chooses a new coordinate system for the data set such that the greatest variance by any projection of the data set comes to lie on...
- Prepare a training set. The faces constituting the training set T should be already prepared for processing.
- Subtract the mean. The average matrix A has to be calculated and subtracted from the original in T. The results are stored in variable S.
- Calculate the covariance matrix.
- Calculate the eigenvectors and eigenvalues of this covariance matrix.
- Choose the principal components.
There will be a large number of eigenfaces created before step 5, and far fewer are really needed. Select from them those that have the highest eigenvalues. For instance, if we are working with a 100 x 100 image, then this system will create 10,000 eigenvectors. Since most individuals can be identified using a database with a size between 100 and 150, most of the 10,000 can be discarded, and only the most important should remain. In statistics and probability theory, the covariance matrix is a matrix of covariances between elements of a vector. ...
In statistics, principal components analysis (PCA) is a technique that can be used to simplify a dataset; more formally it is a linear transformation that chooses a new coordinate system for the data set such that the greatest variance by any projection of the data set comes to lie on...
The eigenfaces that are created will appear as light and dark areas that are arranged in a specific pattern. This pattern is how different features of a face are singled out to be evaluated and scored. There will be a pattern to evaluate symmetry, if there is any style of facial hair, where the hairline is, or evaluate the size of the nose or mouth. Other eigenfaces have patterns that are less simple to identify, and the image of the eigenface may look very little like a face. Sphere symmetry group o. ...
The technique used in creating eigenfaces and using them for recognition is also used outside of facial recognition. This technique is also used for handwriting analysis, lip reading, voice recognition and medical imaging. Therefore, some do not use the term eigenface, but prefer to use 'eigenimage'. Graphology is the study and analysis of handwriting especially in relation to human psychology. ...
Lip reading, also known as lipreading, speech reading, or speechreading, is a technique of understanding speech by visually interpreting the movements of the lips, face and tongue with information provided by the context, language, and any residual hearing. ...
Speaker recognition, or voice recognition is the task of recognizing people from their voices. ...
Medical imaging designates the ensemble of techniques and processes used to create images of the human body (or parts thereof) for clinical purposes (medical procedures seeking to reveal, diagnose or examine disease) or medical science (including the study of normal anatomy and function). ...
Basically, eigenfaces are a set of "standardized face ingredients", derived from statistical analysis of many pictures of faces. Any human face can be considered to be a combination of these standard faces. For example, your face might be composed of 10% from eigenface 1, 55% from eigenface 2, and even -3% from eigenface 3. The values connecting a face to an eigenface can be anywhere from 100% to −100%, the higher the value, the closer the face is to that eigenface. Remarkably, it does not take many eigenfaces summed together to give a fair likeness of most faces. Also, because a person's face is no longer recorded by a digital photograph, but instead as just a list of values (one value for each eigenface in the database used), much less space is taken for each person's face. Statistics is the science and practice of developing knowledge through the use of empirical data expressed in quantitative form. ...
The Nikon Coolpix 950 Casio Exilim Digital photography, as opposed to film photography, uses an electronic sensor to record the image as a piece of electronic data rather than as chemical changes on film. ...
Use in facial recognition Facial recognition was the source of motivation behind the creation of eigenfaces. For this use, eigenfaces have advantages over other techniques available, such as the system's speed and efficiency. Using eigenfaces is very fast, and able to functionally operate on lots of faces in very little time. Unfortunately, this type of facial recognition does have a drawback to consider: trouble recognizing faces when they are viewed with different levels of light or angles. For the system to work well, the faces need to be seen from a frontal view under similar lighting. Face recognition using eigenfaces has been shown to be quite accurate. By experimenting with the system to test it under variations of certain conditions, the following correct recognitions were found: an average of 96% with light variation, 85% with orientation variation, and 64% with size variation. (Turk & Pentland 1991, p. 590)
To complement eigenfaces, another approach has been developed called eigenfeatures. This combines facial metrics (measuring distance between facial features) with the eigenface approach. Another method, which is competing with the eigenface technique uses 'fisherfaces'. This method for facial recognition is less sensitive to variation in lighting and pose of the face than the method using eigenfaces. This page meets Wikipedias criteria for speedy deletion. ...
Research that applies similar eigen techniques to sign language images has also been made. More can be read here: http://www.geigel.com/signlanguage/index.php or the wiki article eigen sign language
See also This article is about process of creating 3D computer graphics. ...
See also: Computer-generated imagery Computer animation is the art of creating moving images via the use of computers. ...
Typical Caucasoid skull Typical Mongoloid skull Typical Negroid skull Craniofacial anthropometry is a technique used in physical anthropology comprising precise and systematic measurement of the bones of the human skull. ...
A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. ...
Variation in the physical appearance of humans is believed by anthropologists to be an important factor in the development of personality and social relations in particular physical attractiveness. ...
Pattern recognition is a field within the area of machine learning. ...
Principal components analysis (PCA) is a technique used to reduce multidimensional data sets to lower dimensions for analysis. ...
References - D. Pissarenko (2003). Eigenface-based facial recognition.
- P. Belhumeur, J. Hespanha, and D. Kriegman (july 1997). "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection". IEEE Transactions on pattern analysis and machine intelligence 19 (7).
- L. Sirovich and M. Kirby (1987). "Low-dimensional procedure for the characterization of human faces". Journal of the Optical Society of America A 4: 519–524.
- M. Kirby and L. Sirovich (1990). "Application of the Karhunen-Loeve procedure for the characterization of human faces". IEEE Transactions on Pattern analysis and Machine Intelligence 12 (1): 103–108.
- M. Turk and A. Pentland (1991). "Face recognition using eigenfaces". Proc. IEEE Conference on Computer Vision and Pattern Recognition: 586–591.
- M. Turk and A. Pentland (1991). "Eigenfaces for recognition". Journal of Cognitive Neuroscience 3 (1): 71–86.
- A. Pentland, B. Moghaddam, T. Starner, O. Oliyide, and M. Turk. (1993). "View-based and modular Eigenspaces for face recognition". Technical Report 245, M.I.T Media Lab.
- T. Heseltine, N. Pears, J. Austin, Z. Chen (2003). "Face Recognition: A Comparison of Appearance-Based Approaches". Proc. VIIth Digital Image Computing: Techniques and Applications, vol 1. 59-68.
External links - Developing Intelligence Eigenfaces and the Fusiform Face Area
- Matlab example code for eigenfaces
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