Disguised face detection and recognition under the complex background
Jing Li, Bin Li, Yong Xu, Kaixuan Lu, Ke Yan, Lunke Fei · 2014
In this paper, we propose an effective method for disguised face detection and recognition under the complex background. This method consists of two stages. The first stage determines whether the object is a person. In this stage, we propose the first-dynamic-then-static foreground object detection strategy. This strategy exploits the updated learning-based codebook model for moving object detection and uses the Local Binary Patterns (LBP) + Histogram of Oriented Gradients (HOG) feature-based head-shoulder detection for static target detection. The second stage determines whether the face is disguised and the classes of disguises. Experiments show that our method can detect disguised faces in real time under the complex background and achieve acceptable disguised face recognition rate.