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  4. Hand-drawn face sketch recognition using rank-level fusion of image quality assessment metrics
 
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Hand-drawn face sketch recognition using rank-level fusion of image quality assessment metrics

Journal
Bulletin of Polish Academy of Sciences
Date Issued
2022
Author(s)
Hadid, Abdenour 
Physics, Mathematics, Computer science 
Mahfoud, Sami
Daamouche, Abdelhamid
Bengherabi, Messaoud
DOI
10.24425/bpasts.2022.143554
URI
https://depot.sorbonne.ae/handle/20.500.12458/1320
Abstract
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.
Subjects
  • Face sketch recogniti...

  • Synthesized face sket...

  • Rank-level fusion

  • IQA metrics

File(s)
 Hand-drawn face sketch recognition using rank-level fusion of image quality.pdf (1.2 MB)
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65
Acquisition Date
Mar 24, 2023
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Acquisition Date
Mar 24, 2023
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