Skip to content Skip to navigation

Connexions

You are here: Home » Content » Thresholds for Eigenface Recognition

Navigation

Content Actions
  • Download module PDF
  • Add to ...
    Add the module to:
    • My Favorites
    • A lens
    • An external social bookmarking service
    • My Favorites (What is 'My Favorites'?)
      'My Favorites' is a special kind of lens which you can use to bookmark modules and collections directly in Connexions. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need a Connexions account to use 'My Favorites'.
    • A lens (What is a lens?)

      Definition of a lens

      Lenses

      A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

      What is in a lens?

      Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

      Who can create a lens?

      Any individual Connexions member, a community, or a respected organization.

    • External bookmarks
  • E-mail the authors

Lenses

What is a lens?

Definition of a lens

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...

Affiliated with (What does "Affiliated with" mean?)

This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • Rice University ELEC 301 Projects

    This module is included inLens: Rice University ELEC 301 Project Lens
    By: Rice University ELEC 301As a part of collection:"ELEC 301 Projects Fall 2004"

    Click the "Rice University ELEC 301 Projects" link to see all content affiliated with them.

Recently Viewed

This feature requires Javascript to be enabled.

Thresholds for Eigenface Recognition

Module by: Jon Krueger, Doug Kochelek, Marshall Robinson, Matthew Escarra

Summary: This describes the basic threshold values that can be computed to determine the identity of a new test image.

When a new image comes into the system, there are three special cases for recognition.

  • Image is a known face in the database
  • Image is a face, but of an unknown person
  • Image is not a face at all. May be a coke can, a door, or an animal.

For a real system, where the pictures are of standard format like a driver’s license photo, the first two cases are useful. In general, the case where one tries to identify a random picture, such a slice of pizza, with a set of faces images is pretty unrealistic. Nonetheless, one can still define these threshold values to characterize the images.

Looking back at the weight matrix of values using M eigenfaces, let’s define the face space as an M-dimensional sphere encompassing all weight vectors in the entire database. A fairly approximate radius of this face space will then be half the diameter of this sphere, or mathematically, half the distance between the furthest points in the sphere.

Figure 1
Figure 1 (radius1.jpg)

θ threshold = 1 2 max⁡( ||Ω− Ω k | | 2 ) θ threshold = 1 2 max⁡( ||Ω− Ω k | | 2 ) (1)

To judge whether a new image falls within this radius, let's calculate the reconstruction error between the image and its reconstruction using M eigenfaces. If the image projects fairly well onto the face space (image follows a face distribution), then the error will be small. However a non face image will almost always lie outside the radius of the face space.

Φ recon = ∑ i=1 M ω i μ i Φ recon = ∑ i=1 M ω i μ i (2)

ε 2 =|| Φ image − Φ recon | | 2 ε 2 =|| Φ image − Φ recon | | 2 (3)

ε> θ threshold ε> θ threshold (4)

If the resulting reconstruction error is greater than the threshold, then the tested image probably is not a face image. Similar thresholds can be calculated for images of like faces. If a image passes the initial face test, it can be compared to the threshold values of faces in the database. A similar match process can be used as mentioned earlier. Also the removal or averaging technique can be applied for detection as previously described.

Comments, questions, feedback, criticisms?

Send feedback