Makeup-Invariant Face Recognition by 3D Face: Modeling and Dual-Tree Complex Wavelet Transform from Women's 2D Real-World Images

摘要

In this paper, a novel feature extraction method is proposed to handle facial makeup in face recognition. To develop a face recognition method robust to facial makeup, features are extracted from face depth in which facial makeup is not effective. Then, face depth features are added to face texture features to perform feature extraction. Accordingly, a 3D face is reconstructed from only a single 2D frontal image with/without facial expressions. Then, the texture and depth of the face are extracted from the reconstructed model. Afterwards, the Dual-Tree Complex Wavelet Transform (DT-CWT) is applied to both texture and reconstructed depth 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). Promising results were achieved for makeup-invariant face recognition on the available image database based on the present method compared to several state-of-the-art methods.