Efficient Face Verification Under Makeup Changes Using Few Salient Regions

Face recognition 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 verification. To deal with this issue, we propose in this work an efficient salient patch-based method for verifying faces under makeup variation. Firstly, we use Mutli-Task Cascaded Convolutional Neural Networks (MTCNN) in order to jointly detect and align the face. Then, we have adapted the O-NET network in order to robustly detect five landmarks by training it on makeup faces. The Histogram of Oriented Gradients (HOG) descriptor and the Local Binary Patterns (LBP) are then used to represent the face by concatenating their histogram features in few salient regions around the detected landmarks. Finally, we estimate the similarity measure between the extracted features in order to compare the two faces while determining whether they are for the same person or not. The performance of the proposed method has been validated on the challenging YMU (YouTube Makeup dataset ) and MIFS (Makeup Induced Face Spoofing) datasets, and the obtained results proved the superiority of the proposed method against relevant multi-patch-based methods from the state of the art.