COST-SENSITIVE SEMI-SUPERVISED DISCRIMINANT ANALYSIS FOR FACE RECOGNITION



Abstract:

In our Project, we present a cost-sensitive semi-supervised discriminant analysis method for face recognition.

In previous methods of dimensionality reduction, they aim to seek low-dimensional feature representations to achieve low classification errors.

In our algorithm, we proposed a new method to learn a discriminative feature subspace by making use of both labeled and unlabeled samples and exploring different cost information of all the training samples simultaneously.