Facial image clustering in stereo videos using local binary patterns and double spectral analysis

Georgios Orfanidis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas

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Abstract

In this work we proposed the use of local binary patterns in combination with double spectral analysis for facial image clustering applied to 3D (stereoscopic) videos. Double spectral clustering involves the fusion of two well known algorithms: Normalized cuts and spectral clustering in order to improve the clustering performance. The use of local binary patterns upon selected fiducial points on the facial images proved to be a good choice for describing images. The framework is applied on 3D videos and makes use of the additional information deriving from
the existence of two channels, left and right for further improving the clustering results.
Original languageEnglish
Title of host publication2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014)
Subtitle of host publicationProceedings of a meeting held 9-12 December 2014, Orlando, Florida, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages217-221
Number of pages5
ISBN (Electronic)9781479945184
ISBN (Print)9781479945177
DOIs
Publication statusPublished - Mar 2015
Event2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014) - Orlando, FL, United States
Duration: 9 Dec 201412 Dec 2014

Conference

Conference2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014)
CountryUnited States
CityOrlando, FL
Period9/12/1412/12/14

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