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 hypothesize 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.
翻译:我们处理的是使用智能手机拍摄的自我视觉图像来估计年龄和性别的问题。部分面部隔离已经成为一个问题,因为必须使用面罩。此外,移动设备的使用已经爆发,这种流行病进一步加速了向数字服务的迁移。然而,在身份或表达识别等相关任务中,最先进的解决方案采用大型进化神经网络,这些网络在移动设备中的使用由于硬件限制和可下载应用程序的尺寸限制而无法进行。为了抵消这一点,我们调整了在图像网络挑战中提议的两部现有的轻量CNN,以及另外两部提议进行移动面部识别的结构。由于使用自动图像进行软生物测量预测的数据集有限,我们通过在图像网络上预先培训的网络来抵制过度适应。此外,一些网络还经过进一步预先培训,以进行面部识别,因为这两个任务都使用类似的输入数据。我们假设,这种战略可能有利于软生物测量估计。关于不同前培训对使用过的网络的影响的全面研究,在经过最精确的场面部之后,已经进行了改进了。