Two-stage approach for pose invariant face recognition

In this work pose-invariant face recognition is attempted using a two-stage approach. In the first stage, the orientation of the face is recognized, and in the second stage, the face is recognized among a subset of faces with the same orientation in the training set. We have generated our own database and tried several different techniques for pose invariant face recognition. We have used both linear techniques such as principal component analysis (PCA) and linear discriminant analysis (LDA) and unsupervised clustering techniques such as C-means and fuzzy C-means. The classification algorithms used with all of these techniques are nearest mean and k-nearest neighbor (k-NN). This work presents a comparison of these techniques on our database. With all techniques, it is observed that the recognition performance is enhanced when view information is incorporated as a preliminary step