|Institution:||The Ohio State University|
|Keywords:||Electrical Engineering; Computer Science|
|Full text PDF:||http://rave.ohiolink.edu/etdc/view?acc_num=osu1429630097|
Face recognition has become more significant in recent years. Facial landmarks, on the other hand, have been proven to be useful in related fields such as face recognition and facial expression analysis. In this paper, we propose a method to identify frontal view faces using facial landmarks, and show that it is robust to 2D rotation, change of scale and change of position. We propose a face descriptor revealing the shape features, the Euclidean distance between landmarks and their mean. We evaluate the effectiveness of the representation by using it to perform face classification on our emotion dataset. We fit a multivariate normal distribution for each identity and construct a Naive Bayes Classifier for classification. We show that our method performs well with a small classification error. Advisors/Committee Members: Martinez, Aleix (Advisor).