Welcome to the InfoSci Platform
Reference Hub1
Combining Block DCV and Support Vector Machine for Ear Recognition

Combining Block DCV and Support Vector Machine for Ear Recognition

Zhao Hailong, Yi Junyan
Copyright: © 2016 |Volume: 8 |Issue: 2 |Pages: 9
ISSN: 1941-8663|EISSN: 1941-8671|EISBN13: 9781466690615|DOI: 10.4018/IJITN.2016040104
Cite Article Cite Article

MLA

Hailong, Zhao, and Yi Junyan. "Combining Block DCV and Support Vector Machine for Ear Recognition." IJITN vol.8, no.2 2016: pp.36-44. http://doi.org/10.4018/IJITN.2016040104

APA

Hailong, Z. & Junyan, Y. (2016). Combining Block DCV and Support Vector Machine for Ear Recognition. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 8(2), 36-44. http://doi.org/10.4018/IJITN.2016040104

Chicago

Hailong, Zhao, and Yi Junyan. "Combining Block DCV and Support Vector Machine for Ear Recognition," International Journal of Interdisciplinary Telecommunications and Networking (IJITN) 8, no.2: 36-44. http://doi.org/10.4018/IJITN.2016040104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.