An area of biometrics that has recently attracted much attention is gender and age classification. Its applications can be found not only in the fields of security and surveillance, but also in the context of marketing and demographic information gathering. In addition, extracting this information from a biometric sample can help to decrease the time to identify the exact individual. In this paper, we exploit pupil size as a discriminating feature for the estimation of gender and age. Data obtained from the free observation of face images have been used to train two classifiers (Adaboost and SVM), considering both the best results produced by each classifier and their fusion through weighted means. With experiments involving more than 100 participants, we have found that pupil size can provide significant results, better than those achievable using data on fixations and gaze paths. Pupil Diameter Mean (PDM) has proved to be the best discriminating feature for both gender and age. To the best of our knowledge, there are no other studies trying to perform such a classification using pupil size only.
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