论文标题
通过更好的基线实现更好的亲属识别
Achieving Better Kinship Recognition Through Better Baseline
论文作者
论文摘要
使用面部图像识别血液关系可以看作是具有额外限制的面部识别系统的应用。事实证明，这些限制很难处理，但是，面对验证的最新进展表明，使用更多的数据和新颖的想法，仍然可以获得很多收益。结果，面部识别是一个很好的源域，我们可以从中转移知识，以在亲属识别中获得更好的性能作为来源域。我们为自动亲属识别任务和基于视网膜面的亲戚搜索[1]提供了一个新的基线，以进行面部注册和Arcface [2]面部验证模型。在上面描述为基础的方法的情况下，我们构建了一条管道，该管道在野外数据挑战中最近认可的家庭的两个曲目中实现了最先进的表现。
Recognizing blood relations using face images can be seen as an application of face recognition systems with additional restrictions. These restrictions proved to be difficult to deal with, however, recent advancements in face verification show that there is still much to gain using more data and novel ideas. As a result face recognition is a great source domain from which we can transfer the knowledge to get better performance in kinship recognition as a source domain. We present a new baseline for an automatic kinship recognition task and relatives search based on RetinaFace[1] for face registration and ArcFace[2] face verification model. With the approach described above as the foundation, we constructed a pipeline that achieved state-of-the-art performance on two tracks in the recent Recognizing Families In the Wild Data Challenge.
