Composite components-based face sketch recognition

Composite face sketch recognition is a challenging problem in face recognition, which is important for law enforcement. Considering composite sketch is generated from facial components, a component-based representation approach (CBR) was proposed to match composite sketch to photos recently. They extract multi-scale local binary pattern (MLBP) feature from every component and use the fusion of four most discriminative components as the final matching score, ignoring the inherent structure of composite sketch and the fusion does not take the full components' information into consideration. This paper presents a novel composite sketch recognition method by extracting scale-invariant feature transform (SIFT) feature and histogram of oriented gradient (HOG) feature from components, fusing different features at score level, combining facial components with linear function. In our proposed method, feature fusion could extract local texture and structure feature from composite sketches. The linear combination not only reserves complete component information but also protrudes different components contribution to the holistic face image. In addition, we show the detailed procedure of composite sketch generation experiment, helping build related face database. Experiments on several public composite sketch databases (including a newly published UoM-SGFS database) demonstrate that our proposed method achieves superior performance compared with state-of-the-art methods.