Biclustering-based Iterative Segmentation of human face images for facial feature extraction

Debby D. Wang, Haoran Xie, Fu Lee Wang, Ran Wang, Xuefei Zhe, Hong Yan

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

With the rapid development of biclustering techniques in machine learning and data mining, such techniques have been successfully applied to practical problems such as gene expression analysis, text mining, collaborative filtering and market analysis. In this work, biclustering techniques were applied to segmentation of gray-scale human face images. A biclustering-based framework (BISA), which iteratively partitions an image into subimages/regions in the SVD subspaces and retains those passing the threshold test as effective regions (ERs), was proposed. After the third iteration of BISA in our experiments, most of important facial feature areas were captured and outputted as ERs, which can be further handled by feature-extraction or contour-detection tools. Overall, the proposed framework is useful and efficient in human face detection and facial feature area extraction, and it welcomes other biclustering methods as components for multi-purpose applications.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1126-1129
Number of pages4
ISBN (Electronic)9781509025961
DOIs
Publication statusPublished - 8 Feb 2017
Externally publishedYes
Event2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
Duration: 22 Nov 201625 Nov 2016

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2016 IEEE Region 10 Conference, TENCON 2016
CountrySingapore
CitySingapore
Period22/11/1625/11/16

Fingerprint Dive into the research topics of 'Biclustering-based Iterative Segmentation of human face images for facial feature extraction'. Together they form a unique fingerprint.

Cite this