We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state‐of‐the‐art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for soft‐biometrics prediction using selfie images are limited, we counteract over‐fitting by using networks pre‐trained on ImageNet. Furthermore, some networks are further pre‐trained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesise that such strategy can be beneficial for soft‐biometrics estimation. A comprehensive study of the effects of different pre‐training over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine‐tuned for face recognition. [ABSTRACT FROM AUTHOR] Copyright of IET Biometrics (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

中文翻译：

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面部面具和软生物特征：利用面部识别 CNN 对移动眼部图像进行年龄和性别预测

我们解决了使用智能手机拍摄的自拍眼睛图像来估计年龄和性别的问题。由于强制使用口罩，部分面部遮挡已成为一个问题。此外，移动设备的使用呈爆炸式增长，大流行进一步加速了向数字服务的迁移。然而，在身份或表情识别等相关任务中，最先进的解决方案采用大型卷积神经网络，由于硬件限制和可下载应用程序的大小限制，其在移动设备中的使用是不可行的。为了解决这个问题，我们采用了在 ImageNet 挑战赛中提出的两个现有轻量级 CNN，以及为移动人脸识别提出的两个额外架构。由于使用自拍图像进行软生物特征预测的数据集是有限的，我们通过使用在 ImageNet 上预训练的网络来抵消过度拟合。此外，一些网络经过进一步的人脸识别预训练，有非常大的训练数据库可供使用。由于这两个任务都使用相似的输入数据，我们假设这种策略可能有利于软生物特征估计。对不同预训练对所采用架构的影响进行了全面研究，表明在大多数情况下，在对网络进行微调以进行人脸识别后，可以获得更好的准确性。[作者摘要] IET Biometrics (Wiley-Blackwell) 的版权是 Wiley-Blackwell 的财产，未经版权所有者明确书面许可，不得将其内容复制或通过电子邮件发送到多个站点或发布到列表服务器。但是，用户可以打印、下载、或电子邮件文章供个人使用。这个摘要可以被删减。不保证副本的准确性。用户应参考材料的原始出版版本以获取完整的摘要。（版权适用于所有摘要。）