结合CNN不同层信息的全变量建模人脸特征表达学习方法

Total Variability Modelling for Face Representation Learning with CNN Multi-Layers

如何学习有效的人脸特征表达是人脸识别的关键性问题。现有基于卷积神经网络(Convolutional Neural Networks,CNN)的人脸深度特征表达学习方法大多在人脸图像经过了有效检测和校正的情况下,能够获得优异的性能,而在复杂场景下其推广性和鲁棒性受到极大限制。对此,本文提出了结合CNN不同层信息的全变量建模人脸特征表达学习方法,将提取的人脸局部深度特征中所包含的差异信息按照子空间进行建模,有效聚合局部深度特征的同时得到人脸在低维子空间的特征表达(i Vector)。在IJB-A(IARPA Janus Benchmark A)上的实验结果表明,与现有的深度特征表达相比,该方法学习得到的人脸iVector表达能够显著提升人脸识别系统的识别性能和计算效率。

Learning face representation is a crucial problem of face recognition system. Recently deep face representation based on convolutional neural networks （CNN） have achieved state-of-the-art performance on public LFW （Labeled Faces in the Wild）. However, the deficiency of generalization and robustness still exists in complex datasets, such as IARPA Ja-nus Benchmark A （IJB-A）. In this paper, a total variability modelling （TVM） method utilizing different output layers of CNN for face representation is proposed. The variations of local deep features can be modeled in the total variability sub-space, which effectively aggregates the local deep features into a compact embedding feature （iVector）. Evaluations on the IJB-A dataset show the proposed face representation learning method, compared with existed face deep representations, has achieved better performance and efficiency.