A face recognition algorithm based on multiple individual discriminative models

Abstract—In this paper, a novel algorithm for facial recognition is proposed. The technique combines the color texture and geometrical configuration provided by face images. Landmarks and pixel intensities are used by Principal Component Analysis and Fisher Linear Discriminant Analysis to associate a one dimensional projection to each person belonging to a reference data set. Each of these projections discriminates the associated person with respect to all other people in the data set. These projections combined with a proposed classification algorithm are able to statistically deciding if a new facial image corresponds to a person in the database. Each projection is also able to visualizing the most discriminative facial features of the person associated to the projection. The performance of the proposed method is tested in two experiments. Results point out the proposed technique as an accurate and robust tool for facial identification and unknown detection.