Efficient Face Verification Under Makeup Using Few Salient Facial Regions (original) (raw)

Automatic recognition of persons has attracted the attention of many researchers during the last years due to its many applications in various fields. However, this task faces several challenges related to many changes that can affect the human face. In particular, make-up faces represent a major challenge for facial recognition and verification. To deal with this issue, we propose an efficient salient patch-based method for verifying faces under makeup variation. Firstly, we use Mutli-Task Cascaded Convolutional Neural Networks (MTCCNN) to jointly, detect and align the face with five landmarks. The Histogram of Oriented Gradients (HOG) descriptor and Local Binary Patterns (LBP) are then adopted to represent the face by concatenating their histogram features in few salient regions around the detected landmarks. Finally, we calculate the similarity measure between the extracted features to compare the two faces and determine whether they are for the same person or not. The performanc...