Summary and Conclusions

---

A variety of non-linear classification techniques have been shown to yield good results for facial feature recognition.

e.g. SVMs, Neural Networks, and Generalized Linear Discriminants.

 

The strategy of augmenting a sparse training set with synthetic faces has been shown to have merit.

The use of synthetic faces in the training set yielded significantly improved classification performance for the tested features of race, age, and beard.