Journal of South China University of Technology (Natural Science Edition) ›› 2008, Vol. 36 ›› Issue (9): 117-121.

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Feature Fusion Method Based on KCCA and Multi-Modality Recognition Fusing Ear and Face Profile Information

Xu Xiao-na1  Mu Zhi-chun2  Pan Xiu-qin1  Zhao Yue1   

  1. 1. School of Information Engineering, Century University for Nationalities, Beijing 100081, China; 2. School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China
  • Received:2007-09-19 Revised:2007-12-15 Online:2008-09-25 Published:2008-09-25
  • Contact: 徐晓娜（1979-），女，博士，主要从事生物特征识别、模式识别研究． E-mail:xu_xiaona@163．com
  • About author:徐晓娜（1979-），女，博士，主要从事生物特征识别、模式识别研究．
  • Supported by:

    国家自然科学基金资助项目（60573058）;北京市教委重点学科共建项目（XK100080537）

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Abstract:

In order to implement the non-intrusive recognition, a multi-modality recognition method of biometric feature is proposed based on the fusion of ear and face profile information. As there is a special physiological location relationship between ear and eye, only the profile-view face images need to be captured for recognition. By introducing the kernel trick to the canonical correlation analysis （CCA） , a feature fusion method based on kernel CCA （KCCA） is established and is then used to capture the associated feature of ear and eye for classification and recognition. Simulated results indicate that the proposed feature fusion method based on KCCA is effective, and that the multi-modality recognition based on ear and face biometrics is of higher performance than the unimodal bio- metric recognition based on ear or face profile. Thus, there comes an effective approach to the non-intrusive biometric recognition.

Key words: ear recognition, multi-modality recognition, feature fusion, canonical correlation analysis, kernel trick, associated feature