Connexions

You are here: Home » Content » Facial Expression Recognition by Support Vector Machines » Algorithm

Recently Viewed

This feature requires Javascript to be enabled.

Inside Collection:

Collection by: Daryl Arredondo, Max Chester, Jiwon Choe, Kai He. E-mail the authors

Algorithm

Algorithm

This method falls under the broad category of machine learning techniques, but the core of the method we used relies on support vector machines (SVM). In general, a SVM takes a set of input data and classifies each data point into one of two general classes. Geometrically, a SVM can be thought of as constructing a plane dividing a region of space into two separate areas, thus new data will fall on one side or the other of this plane indicating its classification. The svm trainer takes a database of training images and creates an svm that can classify input data into one of two types. But we want to deal with more than two types of data, because there are obviously more than two different emotions so what we do is create multiple svms each one specific to a certain emotion and that classifies into either that emotion or not that emotion so then after running it through multiple svms one of them will come up yes and thus identify the emotion. But what if two emotions are identified by two or more different svms one might wonder? Well then one can run the svm predict and obtain probability estimates for each of the classes in a svm. Thus whichever one has the higher probability estimate is the most likely correct.

But there is another mathematical tool which can help improve the accuracy of our program. If we use kernel functions to map the general set of our problem to an inner product set, we can hope to turn the problem into one of linear classifications. For our project, we tried two different kernel functions. The first, the linear kernel, produced the better results for most datasets, while the Radial Basis Function (RBF) kernel didn't produce as accurate results in general. However, for the FEI database the RBF kernel did produce better results suggesting that more testing is necessary to determine what type of kernels are more appropriate for different types of datasets. This might be because the data in the FEI database is less “easy” though how to define this is difficult.

Content actions

PDF | EPUB (?)

What is an EPUB file?

EPUB is an electronic book format that can be read on a variety of mobile devices.

Collection to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens

Lenses

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

What is in a lens?

Lens makers point to 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 member, a community, or a respected organization.

What are tags?

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks

Module to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens

Lenses

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

What is in a lens?

Lens makers point to 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 member, a community, or a respected organization.

What are tags?

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks