Face recognition and authentication using LBP and BSIF mask detection and elimination
In this paper, a face recognition and authentication method that can detect and eliminate the presence of the mask is proposed. The proposed method utilizes facial features, which are captured locally and globally to distinguish between a mask and a real face. The features employed here are extracted from the whole face as well as the eye and nose regions that are expected to provide a clue on the presence of the mask. Here, Local binary pattern (LBP) and Binarized Statistical Image Features (BSIF) are utilized to extract the texture features of the face for recognition. Then for classification Euclidean distance classifier is used whose scores are fused using the weighted sum rule for making the decision about a real face or a mask attack. 3D mask database 3DMAD is used for testing the algorithm.
2016 International Conference on Communication Systems and Networks (ComNet)