3D morphable models application for expanding face database limited to single frontal face image per person

Abstract

Major problem in real world scenarios is lack of suficient image samples per person in database for successful face recognition. In most cases insuficient number of samples per an individual in database is present. This makes face classification almost impossible for larger number of people. This problem is commonly described as 'one sample problem'. Recent state-of-art in face recognition allows to achieve high accuracy using face images with frontal pose. However, recognizing faces with rotations in depth, increases error rate significantly. In this paper we present a method to expand database using 3D morphable models to reconstruct 3D face from a single frontal image sample. By rotating reconstructed face to different views we create series of novel virtual images with pose variations for every individual in database. This approach can help to decrease error rate from pose variations and resolves 'one sample problem'. © 2010 Springer-Verlag Berlin Heidelberg.