Hand-drawn face sketch recognition using rank-level fusion of image quality assessment metrics
Résumé
Face Sketch Recognition (FSR) presents a severe challenge to conventional recognition paradigms developed basically to match
face photos. This challenge is mainly due to the large texture discrepancy between face sketches, characterized by shape exaggeration, and face
photos. In this paper, we propose a training-free synthesized face sketch recognition method based on the rank-level fusion of multiple Image
Quality Assessment (IQA) metrics. The advantages of IQA metrics as a recognition engine are combined with the rank-level fusion to boost the
final recognition accuracy. By integrating multiple IQA metrics into the face sketch recognition framework, the proposed method simultaneously
performs face-sketch matching application and evaluates the performance of face sketch synthesis methods. To test the performance of the recognition framework, five synthesized face sketch methods are used to generate sketches from face photos. We use the Borda count approach to fuse
four IQA metrics, namely, structured similarity index metric, feature similarity index metric, visual information fidelity and gradient magnitude
similarity deviation at the rank-level. Experimental results and comparison with the state-of-the-art methods illustrate the competitiveness of the
proposed synthesized face sketch recognition framework.
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