biometrics face recognition
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Joined: Apr 2010
30-05-2010, 12:19 PM
this project and implimentation is for making a Biometrics - Face Detection , here we Use any standard web camera and try to recognize the face of the person with the detection software in general This can be done by a image acquisition software like (NI labVIEW ), as it has image acquisition tools..
and we Do a general pattern matching detect a face and be able to detect the difference between faces break down the regions of the face and pattern match those regions.and lastly can possible to say this face is belongs some one name (Eg:"Alfred": Aleready feeded photo), or can possible to say this photo matches 78% with Someone (Eg:Alfred)
Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
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Joined: Oct 2010
27-10-2010, 12:40 PM
Can u send me more information on this topic to my mail id below.
Joined: Apr 2012
22-08-2012, 03:58 PM
Face Recognition Using Biometrics
face Recognition.ppt (Size: 1.5 MB / Downloads: 45)
Biometrics means “Life Measurement” it is any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual.
Characteristics of Biometrics
Universality: Each and every person throughout the world should have a biometric characteristic on the basis of which he/she could be represented, for example every person has a thumb impression which is unique with him.
Distinctiveness: Any two persons should be sufficiently different in terms of a biometric characteristic measure. Taking the above example any two persons will have different thumb impression.
Collect-ability: The biometric characteristic can be measured quantitatively with an ease. For example taking a snap of face is typically easy with a camera, where as it is difficult to take a retina sample of a person, but it is possible.
Permanence: The characteristic should be sufficiently invariant (with respect to the matching criterion) over a period of time. Taking an example of face recognition as a person grows in age his or her face changes over a time.
Faces are integral to human interaction
Manual facial recognition is already used in everyday authentication applications
ID Card systems (passports, health card, and driver’s license)
Facial recognition requires 2 steps:
Typical Facial Recognition technology automates the recognition of faces using one of two 2 modeling approaches:
2D Eigen faces
3D Morph able Model
3D Expression Invariant Recognition
Facial Recognition: Eigenface
Decompose face images into a small set of characteristic feature images.
A new face is compared to these stored images.
A match is found if the new faces is close to one of these images.
Facial Recognition: PCA Recognition
A new image is project and implimentation into the “facespace”.
Create a vector of weights that describes this image.
The distance from the original image to this eigenface is compared.
If within certain thresholds then it is a recognized face.
Pros and Cons
2D face recognition methods are sensitive to lighting, head orientations, facial expressions and makeup.
2D images contain limited information
3D Representation of face is less susceptible to isometric deformations (expression changes).
3D approach overcomes problem of large facial orientation changes
Facial scan has unique advantages over other biometrics
Core technologies are highly researched
Automated facial detection and facial recognition algorithm are not yet mature