Evaluation of gender classification methods on thermal and near-infrared face images

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.

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