Fusion of mSSIM and SVM for reduced-reference facial image quality assessment

Authors:
Pengjun Ji;Yuchun Fang;Zhonghua Zhou;Jiazhen Zhu
Affiliations:
School of Computer Engineering and Science, Shanghai University, Shanghai, China;School of Computer Engineering and Science, Shanghai University, Shanghai, China;School of Computer Engineering and Science, Shanghai University, Shanghai, China;School of Computer Engineering and Science, Shanghai University, Shanghai, China
Venue:
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Year:
2012

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Abstract

Image Quality Assessment (IQA) is a critical part in face recognition system for helping to pick out the better quality images to assure high accuracy. In this paper, we propose a simple but efficient facial IQA algorithm based on Bayesian fusion of modified Structural Similarity (mSSIM) index and Support Vector Machine (SVM) as a reduced-reference method for facial IQA. The fusion scheme largely improves the facial IQA and consequently promotes the precision of face recognition when comparing to mSSIM or SVM alone. Experimental validation shows that the proposed algorithm works well in multiple feature spaces on many face databases.