论文标题
通过深度特征衰老的儿童面对年龄的发展
Child Face Age-Progression via Deep Feature Aging
论文作者
论文摘要
考虑到失踪儿童的面部图像画廊，最先进的面部识别系统在识别后来恢复的儿童（探针）方面缺乏。我们提出了一个功能老化模块，可以通过面部匹配器输出年龄的深度面部特征。此外，特征老化模块指导图像空间中的年龄产生，因此可以利用合成的老化面孔来增强任何面部匹配器的纵向识别性能，而无需任何明确的训练。随着时间的流逝，大于10年（在10年或更长时间后发现了失踪的孩子），拟议的年龄制作模块将FaceNet的封闭式识别准确性从16.53％提高到21.44％，而coscece则从60.72％到66.12％，提高到儿童名人数据集的66.12％，即ITWCC。所提出的方法还优于最先进的方法，即排名1识别率为95.91％，而公共老化数据集，FG-NET和99.58％，而CACD-VS上为99.50％，而CACD-VS上的公共老化数据集（99.58％）为94.91％。这些结果表明，衰老的面部特征增强了识别可能是贩运儿童或绑架的受害者的幼儿的能力。
Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age. We propose a feature aging module that can age-progress deep face features output by a face matcher. In addition, the feature aging module guides age-progression in the image space such that synthesized aged faces can be utilized to enhance longitudinal face recognition performance of any face matcher without requiring any explicit training. For time lapses larger than 10 years (the missing child is found after 10 or more years), the proposed age-progression module improves the closed-set identification accuracy of FaceNet from 16.53% to 21.44% and CosFace from 60.72% to 66.12% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-of-the-art approaches with a rank-1 identification rate of 95.91%, compared to 94.91%, on a public aging dataset, FG-NET, and 99.58%, compared to 99.50%, on CACD-VS. These results suggest that aging face features enhances the ability to identify young children who are possible victims of child trafficking or abduction.
