Generating frontal view face image for pose invariant face recognition
Recognizing human faces is one of the most important areas of research in biometrics. However, drastic change of facial poses is a big challenge for its practical application. This paper proposes generating frontal view face image using linear transformation in feature space for face recognition. We extract features from a posed face image using the kernel PCA. Then, we transform the posed face image into its corresponding frontal face image using the transformation matrix predetermined by learning. Then, the generated frontal face image is identified by three different discrimination methods such as LDA, NDA, or GDA. Experimental results show that the recognition rate with the pose transformation outperforms that without pose transformation greatly.
pose invariant face recognition kernel pca pca pose transformation discriminant analysis