[aecs99]

I worked with Chernoff faces long time back and love how this is an interesting way to visualize how discriminative your features are.

The idea is that you take features of your dataset, and use those to represent a face. Say for example, you want to classify 100 people based on different features. And let's say you've collected 15 features for each person (e.g., height, weight, shoulder width, length of first name, length of last name, type of car driven, etc.). Now try mapping each of these features to Chernoff faces. You'd map it in the following manner: height->area of face, weight->shape of face, shoulder width->length of nose, length of first name->location of mouth, length of last name->curve of smile, type of car driven->width of mouth, etc.

Once you've mapped in that fashion and visualize the faces, you can observe how discriminative your features are. How do you interpret this? If your Chernoff faces tend to show a lot of variation in expression (e.g., smiling vs. sad), you say the length of last name is more discriminative. On the other hand, if the faces all appear to have same area, your first feature (i.e., height) is not very discriminative.

Other features used for Chernoff faces could be: location, separation, angle, shape, and width of eyes; location, and width of pupil; location, angle, and width of eyebrow, etc.

One drawback (as listed in the Wikipedia page) is that we humans perceive the importance of these faces by the way in which variables are mapped to the Chernoff facial features. If the feature mapping is not carefully chosen, your largest varying feature may be ignored because we appreciated the change in expression more than the change in eyebrow length.

[amelius]

> this is an interesting way to visualize how discriminative your features are.

I don't get why that is easier or more revealing than doing a principal component analysis (?)

[BjoernKW]

Because recognising faces is an innate human ability while calculating eigenvectors is not.

[aecs99]

I agree. I don't think this isn't any more revealing. Just a different way of visualizing. And there is also the drawback of a whole new interpretation when you re-map your input features to Chernoff facial features.

[OscarCunningham]

Because humans are naturally good at recognizing faces and the differences between them.