Composite Sketch Recognition Using Multi-scale Hog Features and Semantic Attributes

Composite sketch recognition belongs to heterogeneous face recognition research, which is of great important in the field of criminal investigation. Because composite face sketch and photo belong to different modalities, robust representation of face feature cross different modalities is the key to recognition. Considering that composite sketch lacks texture details in some area, using texture features only may result in low recognition accuracy, this paper proposes a composite sketch recognition algorithm based on multi-scale Hog features and semantic attributes. Firstly, the global Hog features of the face and the local Hog features of each face component are extracted to represent the contour and detail features. Then the global and detail features are fused according to their importance at score level. Finally, semantic attributes are employed to reorder the matching results. The proposed algorithm is validated on PRIP-VSGC database and UoM-SGFS database, and achieves rank 10 identification accuracy of 88.6% and 96.7% respectively, which demonstrates that the proposed method outperforms other state-of-the-art methods.