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.

性别和年龄分类是最近引起人们广泛关注的生物识别领域。它的应用不仅可以在安全和监视领域中找到，而且可以在市场营销和人口统计信息收集中找到。此外，从生物特征样本中提取此信息可以帮助减少识别确切个人的时间。在本文中，我们将学生的身材作为估计性别和年龄的区分特征。从自由观察人脸图像获得的数据已用于训练两个分类器（Adaboost和SVM），同时考虑到每个分类器产生的最佳结果以及它们通过加权方式的融合。通过100多个参与者的实验，我们发现瞳孔大小可以提供显着的结果，比使用注视和注视路径上的数据可达到的效果更好。事实证明，小学生直径均值（PDM）是区分性别和年龄的最佳特征。据我们所知，没有其他研究试图仅使用瞳孔大小进行这种分类。