Makeup-insensitive face recognition by facial depth reconstruction and Gabor filter bank from women´s real-world images

In this paper, a new method was proposed to handle facial makeup in face recognition. To improve a face recognition method robust to facial makeup, features were extracted from facial depth in which facial makeup is not effective. Then, face depth features were added to face texture features to perform feature extraction. Accordingly, a 3D face was reconstructed from only a single 2D frontal image with/without facial expressions. Then, the texture and depth of the face were extracted from the reconstructed model. Afterwards, the Gabor Filter Bank (GFB) was 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). Convincing results were achieved for makeup-insensitive face recognition on the available image database based on the present method compared to several state-of-the-art methods.