Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4738
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dc.contributor.authorJoshi, Piyushen_US
dc.contributor.authorPrakash, Suryaen_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-17T15:35:19Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-17T15:35:19Z-
dc.date.issued2014-
dc.identifier.citationJoshi, P., & Prakash, S. (2014). Image quality assessment based on noise detection. Paper presented at the 2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014, 755-759. doi:10.1109/spin.2014.6777055en_US
dc.identifier.isbn9781479928668-
dc.identifier.otherEID(2-s2.0-84902496373)-
dc.identifier.urihttps://doi.org/10.1109/spin.2014.6777055-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/4738-
dc.description.abstractA new technique for image quality assessment based on noise detection is devised in this paper. Many applications like face recognition, ear recognition etc. are dependent on training images. Poor quality training images lead to inefficient training which in turn degrades the performance of a biometric system. This paper has proposed a technique for quality estimation of biometric images. The technique does not need any reference image for image quality assessment. Experimental analysis has shown the efficiency of the proposed technique. © 2014 IEEE.en_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.source2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014en_US
dc.subjectBiometricsen_US
dc.subjectFace recognitionen_US
dc.subjectAbsolute differenceen_US
dc.subjectBiometric recognitionen_US
dc.subjectBiometric systemsen_US
dc.subjectContouren_US
dc.subjectExperimental analysisen_US
dc.subjectImage quality assessmenten_US
dc.subjectQuality estimationen_US
dc.subjectQuality trainingen_US
dc.subjectImage qualityen_US
dc.titleImage quality assessment based on noise detectionen_US
dc.typeConference Paperen_US
Appears in Collections:Department of Computer Science and Engineering

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