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.
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Ji, P., Fang, Y., Zhou, Z., Zhu, J. (2012). Fusion of mSSIM and SVM for Reduced-Reference Facial Image Quality Assessment. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_10
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DOI: https://doi.org/10.1007/978-3-642-35136-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35135-8
Online ISBN: 978-3-642-35136-5
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