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Combining Block DCV and Support Vector Machine for Ear Recognition

Author

Listed:
  • Zhao Hailong

    (Beijing University of Civil Engineering and Architecture, Beijing, China)

  • Yi Junyan

    (Beijing University of Civil Engineering and Architecture, Beijing, China)

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.

Suggested Citation

  • Zhao Hailong & Yi Junyan, 2016. "Combining Block DCV and Support Vector Machine for Ear Recognition," International Journal of Interdisciplinary Telecommunications and Networking (IJITN), IGI Global, vol. 8(2), pages 36-44, April.
    • Handle: RePEc:igg:jitn00:v:8:y:2016:i:2:p:36-44
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