Gender classification using 2-D ear images and sparse representation

Rahman Khorsandi, Mohamed Abdel-Mottaleb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Gender classification attracted the attention of researchers in computer vision for its use in many applications. Researches have addressed this issue based on facial images. In this paper, we present the first approach for gender classification using 2-D ear images based upon sparse representation. In sparse representation, the training data is used to develop a dictionary based on extracted features. In this work, Gabor filters are used for feature extraction. Classification is achieved by representing the test data using the dictionary based upon the extracted features. Experimental results conducted on the University of Notre Dame (UND) collection J dataset, containing large appearance, pose, and lighting variability, yielded gender classification rate of 89.49%.

Original languageEnglish (US)
Title of host publication2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
Pages461-466
Number of pages6
DOIs
StatePublished - Apr 4 2013
Event2013 IEEE Workshop on Applications of Computer Vision, WACV 2013 - Clearwater Beach, FL, United States
Duration: Jan 15 2013Jan 17 2013

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986

Other

Other2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
CountryUnited States
CityClearwater Beach, FL
Period1/15/131/17/13

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Keywords

  • Feature Extraction
  • Gabor Filter
  • Gender classification
  • Sparse Representation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Khorsandi, R., & Abdel-Mottaleb, M. (2013). Gender classification using 2-D ear images and sparse representation. In 2013 IEEE Workshop on Applications of Computer Vision, WACV 2013 (pp. 461-466). [6475055] (Proceedings of IEEE Workshop on Applications of Computer Vision). https://doi.org/10.1109/WACV.2013.6475055