摘要
针对不同年龄跨度下人脸对差异的不同,文中提出基于集成人脸对距离学习(EFPML)的跨年龄人脸验证方法.对不同年龄跨度的人脸对分别学习距离度量,然后使用集成方法对人脸对进行重表示,使人脸对重表示更具有判别性,并且可以扩充有限的跨年龄数据集.在公开的跨年龄人脸数据库FG-NET和CACD上的实验表明,文中方法可以有效减少年龄带来的影响,提高验证性能.
Aiming at the variations of face pairs caused by different age gaps,an ensemble face pairs distance metric learning method(EFPML) is proposed for cross-age face verification. Firstly,the whole dataset is divided into several subsets with different age gaps. Then,a distance metric is learned for each subset.Finally,all face pairs are re-represented for many times via learnt distance metrics, the new representations are more distinguishable and the limited cross-age face data are expanded. To evaluate the proposed method,a series of experiments are conducted on two real-world cross age datasets,FG-NET and CACD. The results show that EFPML consistently outperforms the state-of-the-art methods and it has ability to reduce the effect of aging and improve verification performance.
引文
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