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Proceedings Article

Ethnicity identification from face images

[+] Author Affiliations
Xiaoguang Lu, Anil K. Jain

Michigan State Univ. (USA)

Proc. SPIE 5404, Biometric Technology for Human Identification, 114 (August 25, 2004); doi:10.1117/12.542847
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From Conference Volume 5404

  • Biometric Technology for Human Identification
  • Anil K. Jain; Nalini K. Ratha
  • Orlando, FL | April 12, 2004

abstract

Human facial images provide the demographic information, such as ethnicity and gender. Conversely, ethnicity and gender also play an important role in face-related applications. Image-based ethnicity identification problem is addressed in a machine learning framework. The Linear Discriminant Analysis (LDA) based scheme is presented for the two-class (Asian vs. non-Asian) ethnicity classification task. Multiscale analysis is applied to the input facial images. An ensemble framework, which integrates the LDA analysis for the input face images at different scales, is proposed to further improve the classification performance. The product rule is used as the combination strategy in the ensemble. Experimental results based on a face database containing 263 subjects (2,630 face images, with equal balance between the two classes) are promising, indicating that LDA and the proposed ensemble framework have sufficient discriminative power for the ethnicity classification problem. The normalized ethnicity classification scores can be helpful in the facial identity recognition. Useful as a "soft" biometric, face matching scores can be updated based on the output of ethnicity classification module. In other words, ethnicity classifier does not have to be perfect to be useful in practice.

© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Xiaoguang Lu and Anil K. Jain
"Ethnicity identification from face images", Proc. SPIE 5404, Biometric Technology for Human Identification, 114 (August 25, 2004); doi:10.1117/12.542847; http://dx.doi.org/10.1117/12.542847


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