Iris Image Blur Detection with Multiple Kernel Learning

Lili PAN  Mei XIE  Ling MAO  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.6   pp.1698-1701
Publication Date: 2012/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.1698
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
frequency spectrum,  cepstrum,  multiple kernel learning,  

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In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.