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首页> 外文会议>2011 International Joint Conference on Biometrics >Evaluation of gender classification methods on thermal and near-infrared face images
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Evaluation of gender classification methods on thermal and near-infrared face images

机译：热和近红外面部图像性别分类方法的评估

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摘要

Automatic gender classification based on face images is receiving increased attention in the biometrics community. Most gender classification systems have been evaluated only on face images captured in the visible spectrum. In this work, the possibility of deducing gender from face images obtained in the near-infrared (NIR) and thermal (THM) spectra is established. It is observed that the use of local binary pattern histogram (LBPH) features along with discriminative classifiers results in reasonable gender classification accuracy in both the NIR and THM spectra. Further, the performance of human subjects in classifying thermal face images is studied. Experiments suggest that machine-learning methods are better suited than humans for gender classification from face images in the thermal spectrum.
机译：基于面部图像的自动性别分类在生物识别社区中受到增加的关注。大多数性别分类系统仅在可见光谱中捕获的脸部图像上进行评估。在这项工作中，建立了从近红外（NIR）和热（THM）光谱中获得的面部图像中致法的可能性。观察到局部二进制图案直方图（LBPH）特征以及鉴别类别分类器的使用导致NIR和THM光谱中的合理性分类精度。此外，研究了人类受试者在分类热面图像中的性能。实验表明，从热谱中的面部图像中，机器学习方法比人类对性别分类更适合。

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