Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
This work addresses a domain-specific problem for applications in digital entertainment and law enforcement, but it is incremental as it builds on existing image quality assessment metrics.
The paper tackled the problem of recognizing synthesized face sketches by proposing a framework based on full-reference image quality assessment metrics, achieving effectiveness as demonstrated through extensive experiments compared to baseline methods.
Face sketch synthesis has wide applications ranging from digital entertainments to law enforcements. Objective image quality assessment scores and face recognition accuracy are two mainly used tools to evaluate the synthesis performance. In this paper, we proposed a synthesized face sketch recognition framework based on full-reference image quality assessment metrics. Synthesized sketches generated from four state-of-the-art methods are utilized to test the performance of the proposed recognition framework. For the image quality assessment metrics, we employed the classical structured similarity index metric and other three prevalent metrics: visual information fidelity, feature similarity index metric and gradient magnitude similarity deviation. Extensive experiments compared with baseline methods illustrate the effectiveness of the proposed synthesized face sketch recognition framework. Data and implementation code in this paper are available online at www.ihitworld.com/WNN/IQA_Sketch.zip.