Expression-Invariant Face Recognition via 3D Face Reconstruction Using Gabor Filter Bank from a 2D Single Image

In this paper, a novel method for expression-insensitive face recognition is proposed from only a 2D single image in a gallery including any facial expressions. A 3D Generic Elastic Model (3D GEM) is used to reconstruct a 3D model of each human face in the present database using only a single 2D frontal image with/without facial expressions. Then, the rigid parts of the face are extracted from both the texture and reconstructed depth based on 2D facial land-marks. Afterwards, the Gabor filter bank was applied to the extracted rigid-part of the face to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final feature vectors are generated and classified by the Support Vector Machine (SVM). Favorable outcomes were acquired to handle expression changes on the available image database based on the proposed method compared to several state-of-the-arts in expression-insensitive face recognition.