Abstract
In this paper, we propose a cascaded multimodal biometrics system involving a fusion of frontal face and lateral gait, for the specific problem of ethnicity classification. This system performs human ethnicity classification first from the cues of gait recorded by a long-distance camera and requires next classification using facial images captured by a short-distance camera only when gait based ethnicity identification fails. For gait, we use Gait Energy Image (GEI), a spatio-temporal compact representation of gait in video, to characterize human walking properties. For face, we extract the well-known Gabor feature to render the effective facial appearance information. Experimental results obtained from a database of 22 subjects containing 12 East-Asian and 10 South-American shows that this cascaded system is capable of providing competitive discriminative power on ethnicity with a correct classification rate over 95%.
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Zhang, D., Wang, Y., Zhang, Z. (2012). Ethnicity Classification Based on a Hierarchical Fusion. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_36
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DOI: https://doi.org/10.1007/978-3-642-35136-5_36
Publisher Name: Springer, Berlin, Heidelberg
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