Matching a composite sketch to a photographed face using fused HOG and deep feature models

In this paper, we focus on the research of matching a computer-generated composite face sketch to a photograph. This is of great importance in the field of criminal investigation. To blend the different facial representation modalities, we propose a robust feature model by combining pixel-level features extracted from multi-scale key face patches and high-level features learned from a pre-trained deep learning-based model. At first, texture features are captured by a two-level histogram of oriented gradient descriptor, considering both the overall structure and local details. The semantic-level facial characteristics are analyzed through the high-level features of the Visual Geometry Group-Face (VGG-Face) network. Next, feature similarities between each sketch/photograph pair are measured by feature distance. Then, adaptive weights are assigned to each feature similarity, and score level fused according to their visual saliency contribution. Finally, the fused feature similarity is evaluated for matching purposes. After experimenting on the Pattern Recognition and Image Processing-Viewed Software-Generated Composite (PRIP-VSGC) database and the expanded University of Malta Composite Face Sketch (UoM-SGFS) database, it is found that this framework could achieve more satisfying results compared to the existing methods.

Automated summary

This paper focuses on matching a computer-generated composite face sketch to a photograph, proposing a robust feature model to blend different facial representation modalities for matching purposes. Experimental results show that this framework can achieve more satisfying results compared to existing methods by fusing features from various sources for matching.